Systems and methods relating to bot authoring by mining intents from conversation data via intent seeding

ABSTRACT

A method for authoring a conversational bot including: receiving conversation data; receiving seed intent data that comprises seed intents having a seed intent label and sample intent-bearing utterances; using an intent mining algorithm to mine the conversation data to determine new utterances to associate with the seed intent; augmenting the seed intent data to include the mined new utterances associated with the seed intents; and uploading the augmented seed intent data into the conversation bot. The intent mining algorithm may include: identifying intent-bearing utterances; identifying candidate intents; for each of the seed intents, identifying seed intent alternatives from the sample intent-bearing utterances; associating the intent-bearing utterances from the conversation data with the seed intents via determining a degree of semantic similarity between the candidate intents of the intent-bearing utterances and the seed intent alternatives.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 63/083,561, titled “SYSTEMS AND METHODS RELATING TO BOTAUTHORING AND/OR AUTOMATING THE MINING OF INTENTS FROM NATURAL LANGUAGECONVERSATIONS”, filed in the U.S. Patent and Trademark Office on Sep.25, 2020, the contents of which are incorporated herein.

BACKGROUND

The present invention generally relates to telecommunications systems inthe field of customer relations management including customer assistancevia internet-based service options. More particularly, but not by way oflimitation, the present invention pertains to systems and methods forautomating a bot authoring workflow and/or implementing an intent miningprocess for mining of intents and associated utterances from naturallanguage conversation data using an intent seeding process.

BRIEF DESCRIPTION OF THE INVENTION

The present invention includes a computer-implemented method forauthoring a conversational bot and intent mining using intent seeding isprovided. The method may include: receiving conversation data, theconversation data including text derived from conversations, whereineach of the conversations is between a customer and a customer servicerepresentative; receiving seed intent data that may include seedintents, each of the seed intents including a seed intent label andsample intent-bearing utterances associated with the seed intent; usingan intent mining algorithm to automatically mine the conversation datato determine new utterances to associate with the seed intent;augmenting the seed intent data to include the mined new utterancesassociated with the seed intents; and uploading the augmented seedintent data into the conversation bot and using the conversational botto conduct automated conversations with other customers. The intentmining algorithm may include analyzing utterances occurring within theconversations of the conversation data to identify intent-bearingutterances. The utterances each may include a turn within theconversations whereby the customer, in the form of a customer utterance,or the customer service representative, in the form of a customerservice representative utterance, is communicating. An intent bearingutterance may be defined as one of the utterances determined to have anincreased likelihood of expressing an intent. The intent miningalgorithm may further include analyzing the identified intent-bearingutterances to identify candidate intents. The candidate intents are eachidentified as being a text phrase occurring within one of theintent-bearing utterances that has two parts: an action, which mayinclude a word or phrase describing a purpose or task; and an object,which may include a word or phrase describing an object or thing uponwhich the action operates. The intent mining algorithm may furtherinclude, for each of the seed intents, identifying seed intentalternatives from the sample intent-bearing utterances associated withthe seed intent. The seed intent alternatives are identified as being atext phrase occurring within one of the sample intent-bearing utterancesthat may include two parts: an action, which may include a word orphrase describing a purpose or task; and an object, which may include aword or phrase describing an object or thing upon which the actionoperates. The intent mining algorithm may further include associatingthe intent-bearing utterances from the conversation data with the seedintents via determining a degree of semantic similarity between thecandidate intents present in the intent-bearing utterances and the seedintent alternatives belonging to each of the seed intent labels.

These and other features of the present application will become moreapparent upon review of the following detailed description of theexample embodiments when taken in conjunction with the drawings and theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present invention will become morereadily apparent as the invention becomes better understood by referenceto the following detailed description when considered in conjunctionwith the accompanying drawings, in which like reference symbols indicatelike components, wherein:

FIG. 1 depicts a schematic block diagram of a computing device inaccordance with exemplary embodiments of the present invention and/orwith which exemplary embodiments of the present invention may be enabledor practiced;

FIG. 2 depicts a schematic block diagram of a communicationsinfrastructure or contact center in accordance with exemplaryembodiments of the present invention and/or with which exemplaryembodiments of the present invention may be enabled or practiced;

FIG. 3 is schematic block diagram showing further details of a chatserver operating as part of the chat system according to embodiments ofthe present invention;

FIG. 4 is a schematic block diagram of a chat module according toembodiments of the present invention;

FIG. 5 is an exemplary customer chat interface according to embodimentsof the present invention;

FIG. 6 is a block diagram of a customer automation system according toembodiments of the present invention;

FIG. 7 is a flowchart of a method for automating an interaction onbehalf of a customer according to embodiments of the present invention;

FIG. 8 is a workflow for authoring a conversational bot;

FIG. 9 is an exemplary flowchart for intent mining in accordance withthe present invention; and

FIG. 10 is an exemplary flowchart for intent mining via seeding intentsin accordance with the present invention.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of theinvention, reference will now be made to the exemplary embodimentsillustrated in the drawings and specific language will be used todescribe the same. It will be apparent, however, to one having ordinaryskill in the art that the detailed material provided in the examples maynot be needed to practice the present invention. In other instances,well-known materials or methods have not been described in detail inorder to avoid obscuring the present invention. Additionally, furthermodification in the provided examples or application of the principlesof the invention, as presented herein, are contemplated as wouldnormally occur to those skilled in the art.

As used herein, language designating nonlimiting examples andillustrations includes “e.g.”, “i.e.”, “for example”, “for instance” andthe like. Further, reference throughout this specification to “anembodiment”, “one embodiment”, “present embodiments”, “exemplaryembodiments”, “certain embodiments” and the like means that a particularfeature, structure or characteristic described in connection with thegiven example may be included in at least one embodiment of the presentinvention. Thus, appearances of the phrases “an embodiment”, “oneembodiment”, “present embodiments”, “exemplary embodiments”, “certainembodiments” and the like are not necessarily referring to the sameembodiment or example. Further, particular features, structures orcharacteristics may be combined in any suitable combinations and/orsub-combinations in one or more embodiments or examples.

Those skilled in the art will recognize from the present disclosure thatthe various embodiments may be computer implemented using many differenttypes of data processing equipment, with embodiments being implementedas an apparatus, method, or computer program product. Exampleembodiments, thus, may take the form of an entirely hardware embodiment,an entirely software embodiment, or an embodiment combining software andhardware aspects. Example embodiments further may take the form of acomputer program product embodied by computer-usable program code in anytangible medium of expression. In each case, the example embodiment maybe generally referred to as a “module”, “system”, or “method”.

The flowcharts and block diagrams provided in the figures illustratearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products in accordance withexample embodiments of the present invention. In this regard, it will beunderstood that each block of the flowcharts and/or block diagrams—orcombinations of those blocks—may represent a module, segment, or portionof program code having one or more executable instructions forimplementing the specified logical functions. It will similarly beunderstood that each of block of the flowcharts and/or block diagrams—orcombinations of those blocks—may be implemented by special purposehardware-based systems or combinations of special purpose hardware andcomputer instructions performing the specified acts or functions. Suchcomputer program instructions also may be stored in a computer-readablemedium that can direct a computer or other programmable data processingapparatus to function in a particular manner, such that the programinstructions in the computer-readable medium produces an article ofmanufacture that includes instructions by which the functions or actsspecified in each block of the flowcharts and/or block diagrams—orcombinations of those blocks—are implemented.

Computing Device

It will be appreciated that the systems and methods of the presentinvention may be computer implemented using many different forms of dataprocessing equipment, for example, digital microprocessors andassociated memory, executing appropriate software programs. By way ofbackground, FIG. 1 illustrates a schematic block diagram of an exemplarycomputing device 100 in accordance with embodiments of the presentinvention and/or with which those embodiments may be enabled orpracticed. It should be understood that FIG. 1 is provided as anon-limiting example.

The computing device 100, for example, may be implemented via firmware(e.g., an application-specific integrated circuit), hardware, or acombination of software, firmware, and hardware. It will be appreciatedthat each of the servers, controllers, switches, gateways, engines,and/or modules in the following figures (which collectively may bereferred to as servers or modules) may be implemented via one or more ofthe computing devices 100. As an example, the various servers may be aprocess running on one or more processors of one or more computingdevices 100, which may be executing computer program instructions andinteracting with other systems or modules in order to perform thevarious functionalities described herein. Unless otherwise specificallylimited, the functionality described in relation to a plurality ofcomputing devices may be integrated into a single computing device, orthe various functionalities described in relation to a single computingdevice may be distributed across several computing devices. Further, inrelation to the computing systems described in the followingfigures—such as, for example, the contact center system 200 of FIG.2—the various servers and computer devices thereof may be located onlocal computing devices 100 (i.e., on-site or at the same physicallocation as contact center agents), remote computing devices 100 (i.e.,off-site or in a cloud computing environment, for example, in a remotedata center connected to the contact center via a network), or somecombination thereof. Functionality provided by servers located onoff-site computing devices may be accessed and provided over a virtualprivate network (VPN), as if such servers were on-site, or thefunctionality may be provided using a software as a service (SaaS)accessed over the Internet using various protocols, such as byexchanging data via extensible markup language (XML), JSON, and thelike.

As shown in the illustrated example, the computing device 100 mayinclude a central processing unit (CPU) or processor 105 and a mainmemory 110. The computing device 100 may also include a storage device115, removable media interface 120, network interface 125, I/Ocontroller 130, and one or more input/output (I/O) devices 135, which asdepicted may include an, display device 135A, keyboard 135B, andpointing device 135C. The computing device 100 further may includeadditional elements, such as a memory port 140, a bridge 145, I/O ports,one or more additional input/output devices 135D, 135E, 135F, and acache memory 150 in communication with the processor 105.

The processor 105 may be any logic circuitry that responds to andprocesses instructions fetched from the main memory 110. For example,the process 105 may be implemented by an integrated circuit, e.g., amicroprocessor, microcontroller, or graphics processing unit, or in afield-programmable gate array or application-specific integratedcircuit. As depicted, the processor 105 may communicate directly withthe cache memory 150 via a secondary bus or backside bus. The cachememory 150 typically has a faster response time than main memory 110.The main memory 110 may be one or more memory chips capable of storingdata and allowing stored data to be directly accessed by the centralprocessing unit 105. The storage device 115 may provide storage for anoperating system, which controls scheduling tasks and access to systemresources, and other software. Unless otherwise limited, the computingdevice 100 may include an operating system and software capable ofperforming the functionality described herein.

As depicted in the illustrated example, the computing device 100 mayinclude a wide variety of I/O devices 135, one or more of which may beconnected via the I/O controller 130. Input devices, for example, mayinclude a keyboard 135B and a pointing device 135C, e.g., a mouse oroptical pen. Output devices, for example, may include video displaydevices, speakers, and printers. The I/O devices 135 and/or the I/Ocontroller 130 may include suitable hardware and/or software forenabling the use of multiple display devices. The computing device 100may also support one or more removable media interfaces 120, such as adisk drive, USB port, or any other device suitable for reading data fromor writing data to computer readable media. More generally, the I/Odevices 135 may include any conventional devices for performing thefunctionality described herein.

The computing device 100 may be any workstation, desktop computer,laptop or notebook computer, server machine, virtualized machine, mobileor smart phone, portable telecommunication device, media playing device,gaming system, mobile computing device, or any other type of computing,telecommunications or media device, without limitation, capable ofperforming the operations and functionality described herein. Thecomputing device 100 include a plurality of devices connected by anetwork or connected to other systems and resources via a network. Asused herein, a network includes one or more computing devices, machines,clients, client nodes, client machines, client computers, clientdevices, endpoints, or endpoint nodes in communication with one or moreother computing devices, machines, clients, client nodes, clientmachines, client computers, client devices, endpoints, or endpointnodes. For example, the network may be a private or public switchedtelephone network (PSTN), wireless carrier network, local area network(LAN), private wide area network (WAN), public WAN such as the Internet,etc., with connections being established using appropriate communicationprotocols. More generally, it should be understood that, unlessotherwise limited, the computing device 100 may communicate with othercomputing devices 100 via any type of network using any conventionalcommunication protocol. Further, the network may be a virtual networkenvironment where various network components are virtualized. Forexample, the various machines may be virtual machines implemented as asoftware-based computer running on a physical machine, or a “hypervisor”type of virtualization may be used where multiple virtual machines runon the same host physical machine. Other types of virtualization arealso contemplated.

Contact Center

With reference now to FIG. 2, a communications infrastructure or contactcenter system 200 is shown in accordance with exemplary embodiments ofthe present invention and/or with which exemplary embodiments of thepresent invention may be enabled or practiced. It should be understoodthat the term “contact center system” is used herein to refer to thesystem depicted in FIG. 2 and/or the components thereof, while the term“contact center” is used more generally to refer to contact centersystems, customer service providers operating those systems, and/or theorganizations or enterprises associated therewith. Thus, unlessotherwise specifically limited, the term “contact center” refersgenerally to a contact center system (such as the contact center system200), the associated customer service provider (such as a particularcustomer service provider providing customer services through thecontact center system 200), as well as the organization or enterprise onbehalf of which those customer services are being provided.

By way of background, customer service providers generally offer manytypes of services through contact centers. Such contact centers may bestaffed with employees or customer service agents (or simply “agents”),with the agents serving as an interface between a company, enterprise,government agency, or organization (hereinafter referred tointerchangeably as an “organization” or “enterprise”) and persons, suchas users, individuals, or customers (hereinafter referred tointerchangeably as “individuals” or “customers”). For example, theagents at a contact center may assist customers in making purchasingdecisions, receiving orders, or solving problems with products orservices already received. Within a contact center, such interactionsbetween contact center agents and outside entities or customers may beconducted over a variety of communication channels, such as, forexample, via voice (e.g., telephone calls or voice over IP or VoIPcalls), video (e.g., video conferencing), text (e.g., emails and textchat), screen sharing, co-browsing, or the like.

Operationally, contact centers generally strive to provide qualityservices to customers while minimizing costs. For example, one way for acontact center to operate is to handle every customer interaction with alive agent. While this approach may score well in terms of the servicequality, it likely would also be prohibitively expensive due to the highcost of agent labor. Because of this, most contact centers utilize somelevel of automated processes in place of live agents, such as, forexample, interactive voice response (IVR) systems, interactive mediaresponse (IMR) systems, internet robots or “bots”, automated chatmodules or “chatbots”, and the like. In many cases this has proven to bea successful strategy, as automated processes can be highly efficient inhandling certain types of interactions and effective at decreasing theneed for live agents. Such automation allows contact centers to targetthe use of human agents for the more difficult customer interactions,while the automated processes handle the more repetitive or routinetasks. Further, automated processes can be structured in a way thatoptimizes efficiency and promotes repeatability. Whereas a human or liveagent may forget to ask certain questions or follow-up on particulardetails, such mistakes are typically avoided through the use ofautomated processes. While customer service providers are increasinglyrelying on automated processes to interact with customers, the use ofsuch technologies by customers remains far less developed. Thus, whileIVR systems, IMR systems, and/or bots are used to automate portions ofthe interaction on the contact center-side of an interaction, theactions on the customer-side remain for the customer to performmanually.

Referring specifically to FIG. 2, the contact center system 200 may beused by a customer service provider to provide various types of servicesto customers. For example, the contact center system 200 may be used toengage and manage interactions in which automated processes (or bots) orhuman agents communicate with customers. As should be understood, thecontact center system 200 may be an in-house facility to a business orenterprise for performing the functions of sales and customer servicerelative to products and services available through the enterprise. Inanother aspect, the contact center system 200 may be operated by athird-party service provider that contracts to provide services foranother organization. Further, the contact center system 200 may bedeployed on equipment dedicated to the enterprise or third-party serviceprovider, and/or deployed in a remote computing environment such as, forexample, a private or public cloud environment with infrastructure forsupporting multiple contact centers for multiple enterprises. Thecontact center system 200 may include software applications or programs,which may be executed on premises or remotely or some combinationthereof. It should further be appreciated that the various components ofthe contact center system 200 may be distributed across variousgeographic locations and not necessarily contained in a single locationor computing environment.

It should further be understood that, unless otherwise specificallylimited, any of the computing elements of the present invention may beimplemented in cloud-based or cloud computing environments. As usedherein, “cloud computing”—or, simply, the “cloud”—is defined as a modelfor enabling ubiquitous, convenient, on-demand network access to ashared pool of configurable computing resources (e.g., networks,servers, storage, applications, and services) that can be rapidlyprovisioned via virtualization and released with minimal managementeffort or service provider interaction, and then scaled accordingly.Cloud computing can be composed of various characteristics (e.g.,on-demand self-service, broad network access, resource pooling, rapidelasticity, measured service, etc.), service models (e.g., Software as aService (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as aService (“IaaS”), and deployment models (e.g., private cloud, communitycloud, public cloud, hybrid cloud, etc.). Often referred to as a“serverless architecture”, a cloud execution model generally includes aservice provider dynamically managing an allocation and provisioning ofremote servers for achieving a desired functionality.

In accordance with the illustrated example of FIG. 2, the components ormodules of the contact center system 200 may include: a plurality ofcustomer devices 205A, 205B, 205C; communications network (or simply“network”) 210; switch/media gateway 212; call controller 214;interactive media response (IMR) server 216; routing server 218; storagedevice 220; statistics (or “stat”) server 226; plurality of agentdevices 230A, 230B, 230C that include workbins 232A, 232B, 232C,respectively; multimedia/social media server 234; knowledge managementserver 236 coupled to a knowledge system 238; chat server 240; webservers 242; interaction (or “iXn”) server 244; universal contact server(or “UCS”) 246; reporting server 248; media services server 249; andanalytics module 250. It should be understood that any of thecomputer-implemented components, modules, or servers described inrelation to FIG. 2 or in any of the following figures may be implementedvia types of computing devices, such as, for example, the computingdevice 100 of FIG. 1. As will be seen, the contact center system 200generally manages resources (e.g., personnel, computers,telecommunication equipment, etc.) to enable delivery of services viatelephone, email, chat, or other communication mechanisms. Such servicesmay vary depending on the type of contact center and, for example, mayinclude customer service, help desk functionality, emergency response,telemarketing, order taking, and the like.

Customers desiring to receive services from the contact center system200 may initiate inbound communications (e.g., telephone calls, emails,chats, etc.) to the contact center system 200 via a customer device 205.While FIG. 2 shows three such customer devices—i.e., customer devices205A, 205B, and 205C—it should be understood that any number may bepresent. The customer devices 205, for example, may be a communicationdevice, such as a telephone, smart phone, computer, tablet, or laptop.In accordance with functionality described herein, customers maygenerally use the customer devices 205 to initiate, manage, and conductcommunications with the contact center system 200, such as telephonecalls, emails, chats, text messages, web-browsing sessions, and othermulti-media transactions.

Inbound and outbound communications from and to the customer devices 205may traverse the network 210, with the nature of network typicallydepending on the type of customer device being used and form ofcommunication. As an example, the network 210 may include acommunication network of telephone, cellular, and/or data services. Thenetwork 210 may be a private or public switched telephone network(PSTN), local area network (LAN), private wide area network (WAN),and/or public WAN such as the Internet. Further, the network 210 mayinclude a wireless carrier network including a code division multipleaccess (CDMA) network, global system for mobile communications (GSM)network, or any wireless network/technology conventional in the art,including but not limited to 3G, 4G, LTE, 5G, etc.

In regard to the switch/media gateway 212, it may be coupled to thenetwork 210 for receiving and transmitting telephone calls betweencustomers and the contact center system 200. The switch/media gateway212 may include a telephone or communication switch configured tofunction as a central switch for agent level routing within the center.The switch may be a hardware switching system or implemented viasoftware. For example, the switch 215 may include an automatic calldistributor, a private branch exchange (PBX), an IP-based softwareswitch, and/or any other switch with specialized hardware and softwareconfigured to receive Internet-sourced interactions and/or telephonenetwork-sourced interactions from a customer, and route thoseinteractions to, for example, one of the agent devices 230. Thus, ingeneral, the switch/media gateway 212 establishes a voice connectionbetween the customer and the agent by establishing a connection betweenthe customer device 205 and agent device 230.

As further shown, the switch/media gateway 212 may be coupled to thecall controller 214 which, for example, serves as an adapter orinterface between the switch and the other routing, monitoring, andcommunication-handling components of the contact center system 200. Thecall controller 214 may be configured to process PSTN calls, VoIP calls,etc. For example, the call controller 214 may include computer-telephoneintegration (CTI) software for interfacing with the switch/media gatewayand other components. The call controller 214 may include a sessioninitiation protocol (SIP) server for processing SIP calls. The callcontroller 214 may also extract data about an incoming interaction, suchas the customer's telephone number, IP address, or email address, andthen communicate these with other contact center components inprocessing the interaction.

In regard to the interactive media response (IMR) server 216, it may beconfigured to enable self-help or virtual assistant functionality.Specifically, the IMR server 216 may be similar to an interactive voiceresponse (IVR) server, except that the IMR server 216 is not restrictedto voice and may also cover a variety of media channels. In an exampleillustrating voice, the IMR server 216 may be configured with an IMRscript for querying customers on their needs. For example, a contactcenter for a bank may tell customers via the IMR script to “press 1” ifthey wish to retrieve their account balance. Through continuedinteraction with the IMR server 216, customers may receive servicewithout needing to speak with an agent. The IMR server 216 may also beconfigured to ascertain why a customer is contacting the contact centerso that the communication may be routed to the appropriate resource.

In regard to the routing server 218, it may function to route incominginteractions. For example, once it is determined that an inboundcommunication should be handled by a human agent, functionality withinthe routing server 218 may select the most appropriate agent and routethe communication thereto. This agent selection may be based on whichavailable agent is best suited for handling the communication. Morespecifically, the selection of appropriate agent may be based on arouting strategy or algorithm that is implemented by the routing server218. In doing this, the routing server 218 may query data that isrelevant to the incoming interaction, for example, data relating to theparticular customer, available agents, and the type of interaction,which, as described more below, may be stored in particular databases.Once the agent is selected, the routing server 218 may interact with thecall controller 214 to route (i.e., connect) the incoming interaction tothe corresponding agent device 230. As part of this connection,information about the customer may be provided to the selected agent viatheir agent device 230. This information is intended to enhance theservice the agent is able to provide to the customer.

Regarding data storage, the contact center system 200 may include one ormore mass storage devices—represented generally by the storage device220—for storing data in one or more databases relevant to thefunctioning of the contact center. For example, the storage device 220may store customer data that is maintained in a customer database 222.Such customer data may include customer profiles, contact information,service level agreement (SLA), and interaction history (e.g., details ofprevious interactions with a particular customer, including the natureof previous interactions, disposition data, wait time, handle time, andactions taken by the contact center to resolve customer issues). Asanother example, the storage device 220 may store agent data in an agentdatabase 223. Agent data maintained by the contact center system 200 mayinclude agent availability and agent profiles, schedules, skills, handletime, etc. As another example, the storage device 220 may storeinteraction data in an interaction database 224. Interaction data mayinclude data relating to numerous past interactions between customersand contact centers. More generally, it should be understood that,unless otherwise specified, the storage device 220 may be configured toinclude databases and/or store data related to any of the types ofinformation described herein, with those databases and/or data beingaccessible to the other modules or servers of the contact center system200 in ways that facilitate the functionality described herein. Forexample, the servers or modules of the contact center system 200 mayquery such databases to retrieve data stored therewithin or transmitdata thereto for storage. The storage device 220, for example, may takethe form of any conventional storage medium and may be locally housed oroperated from a remote location. As an example, the databases may beCassandra database, NoSQL database, or a SQL database and managed by adatabase management system, such as, Oracle, IBM DB2, Microsoft SQLserver, or Microsoft Access, PostgreSQL.

In regard to the stat server 226, it may be configured to record andaggregate data relating to the performance and operational aspects ofthe contact center system 200. Such information may be compiled by thestat server 226 and made available to other servers and modules, such asthe reporting server 248, which then may use the data to produce reportsthat are used to manage operational aspects of the contact center andexecute automated actions in accordance with functionality describedherein. Such data may relate to the state of contact center resources,e.g., average wait time, abandonment rate, agent occupancy, and othersas functionality described herein would require.

The agent devices 230 of the contact center 200 may be communicationdevices configured to interact with the various components and modulesof the contact center system 200 in ways that facilitate functionalitydescribed herein. An agent device 230, for example, may include atelephone adapted for regular telephone calls or VoIP calls. An agentdevice 230 may further include a computing device configured tocommunicate with the servers of the contact center system 200, performdata processing associated with operations, and interface with customersvia voice, chat, email, and other multimedia communication mechanismsaccording to functionality described herein. While FIG. 2 shows threesuch agent devices—i.e., agent devices 230A, 230B and 230C—it should beunderstood that any number may be present.

In regard to the multimedia/social media server 234, it may beconfigured to facilitate media interactions (other than voice) with thecustomer devices 205 and/or the servers 242. Such media interactions maybe related, for example, to email, voice mail, chat, video,text-messaging, web, social media, co-browsing, etc. Themulti-media/social media server 234 may take the form of any IP routerconventional in the art with specialized hardware and software forreceiving, processing, and forwarding multi-media events andcommunications.

In regard to the knowledge management server 234, it may be configuredfacilitate interactions between customers and the knowledge system 238.In general, the knowledge system 238 may be a computer system capable ofreceiving questions or queries and providing answers in response. Theknowledge system 238 may be included as part of the contact centersystem 200 or operated remotely by a third party. The knowledge system238 may include an artificially intelligent computer system capable ofanswering questions posed in natural language by retrieving informationfrom information sources such as encyclopedias, dictionaries, newswirearticles, literary works, or other documents submitted to the knowledgesystem 238 as reference materials, as is known in the art. As anexample, the knowledge system 238 may be embodied as IBM Watson or alike system.

In regard to the chat server 240, it may be configured to conduct,orchestrate, and manage electronic chat communications with customers.In general, the chat server 240 is configured to implement and maintainchat conversations and generate chat transcripts. Such chatcommunications may be conducted by the chat server 240 in such a waythat a customer communicates with automated chatbots, human agents, orboth. In exemplary embodiments, the chat server 240 may perform as achat orchestration server that dispatches chat conversations among thechatbots and available human agents. In such cases, the processing logicof the chat server 240 may be rules driven so to leverage an intelligentworkload distribution among available chat resources. The chat server240 further may implement, manage and facilitate user interfaces (alsoUIs) associated with the chat feature, including those UIs generated ateither the customer device 205 or the agent device 230. The chat server240 may be configured to transfer chats within a single chat sessionwith a particular customer between automated and human sources suchthat, for example, a chat session transfers from a chatbot to a humanagent or from a human agent to a chatbot. The chat server 240 may alsobe coupled to the knowledge management server 234 and the knowledgesystems 238 for receiving suggestions and answers to queries posed bycustomers during a chat so that, for example, links to relevant articlescan be provided.

In regard to the web servers 242, such servers may be included toprovide site hosts for a variety of social interaction sites to whichcustomers subscribe, such as Facebook, Twitter, Instagram, etc. Thoughdepicted as part of the contact center system 200, it should beunderstood that the web servers 242 may be provided by third partiesand/or maintained remotely. The web servers 242 may also providewebpages for the enterprise or organization being supported by thecontact center system 200. For example, customers may browse thewebpages and receive information about the products and services of aparticular enterprise. Within such enterprise webpages, mechanisms maybe provided for initiating an interaction with the contact center system200, for example, via web chat, voice, or email. An example of such amechanism is a widget, which can be deployed on the webpages or websiteshosted on the web servers 242. As used herein, a widget refers to a userinterface component that performs a particular function. In someimplementations, a widget may include a graphical user interface controlthat can be overlaid on a webpage displayed to a customer via theInternet. The widget may show information, such as in a window or textbox, or include buttons or other controls that allow the customer toaccess certain functionalities, such as sharing or opening a file orinitiating a communication. In some implementations, a widget includes auser interface component having a portable portion of code that can beinstalled and executed within a separate webpage without compilation.Some widgets can include corresponding or additional user interfaces andbe configured to access a variety of local resources (e.g., a calendaror contact information on the customer device) or remote resources vianetwork (e.g., instant messaging, electronic mail, or social networkingupdates).

In regard to the interaction (iXn) server 244, it may be configured tomanage deferrable activities of the contact center and the routingthereof to human agents for completion. As used herein, deferrableactivities include back-office work that can be performed off-line,e.g., responding to emails, attending training, and other activitiesthat do not entail real-time communication with a customer. As anexample, the interaction (iXn) server 244 may be configured to interactwith the routing server 218 for selecting an appropriate agent to handleeach of the deferable activities. Once assigned to a particular agent,the deferable activity is pushed to that agent so that it appears on theagent device 230 of the selected agent. The deferable activity mayappear in a workbin 232 as a task for the selected agent to complete.The functionality of the workbin 232 may be implemented via anyconventional data structure, such as, for example, a linked list, array,etc. Each of the agent devices 230 may include a workbin 232, with theworkbins 232A, 232B, and 232C being maintained in the agent devices230A, 230B, and 230C, respectively. As an example, a workbin 232 may bemaintained in the buffer memory of the corresponding agent device 230.

In regard to the universal contact server (UCS) 246, it may beconfigured to retrieve information stored in the customer database 222and/or transmit information thereto for storage therein. For example,the UCS 246 may be utilized as part of the chat feature to facilitatemaintaining a history on how chats with a particular customer werehandled, which then may be used as a reference for how future chatsshould be handled. More generally, the UCS 246 may be configured tofacilitate maintaining a history of customer preferences, such aspreferred media channels and best times to contact. To do this, the UCS246 may be configured to identify data pertinent to the interactionhistory for each customer such as, for example, data related to commentsfrom agents, customer communication history, and the like. Each of thesedata types then may be stored in the customer database 222 or on othermodules and retrieved as functionality described herein requires.

In regard to the reporting server 248, it may be configured to generatereports from data compiled and aggregated by the statistics server 226or other sources. Such reports may include near real-time reports orhistorical reports and concern the state of contact center resources andperformance characteristics, such as, for example, average wait time,abandonment rate, agent occupancy. The reports may be generatedautomatically or in response to specific requests from a requestor(e.g., agent, administrator, contact center application, etc.). Thereports then may be used toward managing the contact center operationsin accordance with functionality described herein.

In regard to the media services server 249, it may be configured toprovide audio and/or video services to support contact center features.In accordance with functionality described herein, such features mayinclude prompts for an IVR or IMR system (e.g., playback of audiofiles), hold music, voicemails/single party recordings, multi-partyrecordings (e.g., of audio and/or video calls), speech recognition, dualtone multi frequency (DTMF) recognition, faxes, audio and videotranscoding, secure real-time transport protocol (SRTP), audioconferencing, video conferencing, coaching (e.g., support for a coach tolisten in on an interaction between a customer and an agent and for thecoach to provide comments to the agent without the customer hearing thecomments), call analysis, keyword spotting, and the like.

In regard to the analytics module 250, it may be configured to providesystems and methods for performing analytics on data received from aplurality of different data sources as functionality described hereinmay require. In accordance with example embodiments, the analyticsmodule 250 also may generate, update, train, and modify predictors ormodels 252 based on collected data, such as, for example, customer data,agent data, and interaction data. The models 252 may include behaviormodels of customers or agents. The behavior models may be used topredict behaviors of, for example, customers or agents, in a variety ofsituations, thereby allowing embodiments of the present invention totailor interactions based on such predictions or to allocate resourcesin preparation for predicted characteristics of future interactions,thereby improving overall contact center performance and the customerexperience. It will be appreciated that, while the analytics module 250is depicted as being part of a contact center, such behavior models alsomay be implemented on customer systems (or, as also used herein, on the“customer-side” of the interaction) and used for the benefit ofcustomers.

According to exemplary embodiments, the analytics module 250 may haveaccess to the data stored in the storage device 220, including thecustomer database 222 and agent database 223. The analytics module 250also may have access to the interaction database 224, which stores datarelated to interactions and interaction content (e.g., transcripts ofthe interactions and events detected therein), interaction metadata(e.g., customer identifier, agent identifier, medium of interaction,length of interaction, interaction start and end time, department,tagged categories), and the application setting (e.g., the interactionpath through the contact center). Further, as discussed more below, theanalytic module 250 may be configured to retrieve data stored within thestorage device 220 for use in developing and training algorithms andmodels 252, for example, by applying machine learning techniques.

One or more of the included models 252 may be configured to predictcustomer or agent behavior and/or aspects related to contact centeroperation and performance. Further, one or more of the models 252 may beused in natural language processing and, for example, include intentrecognition and the like. The models 252 may be developed based upon 1)known first principle equations describing a system, 2) data, resultingin an empirical model, or 3) a combination of known first principleequations and data. In developing a model for use with presentembodiments, because first principles equations are often not availableor easily derived, it may be generally preferred to build an empiricalmodel based upon collected and stored data. To properly capture therelationship between the manipulated/disturbance variables and thecontrolled variables of complex systems, it may be preferable that themodels 252 are nonlinear. This is because nonlinear models can representcurved rather than straight-line relationships betweenmanipulated/disturbance variables and controlled variables, which arecommon to complex systems such as those discussed herein. Given theforegoing requirements, a machine learning or neural network-basedapproach is presently a preferred embodiment for implementing the models252. Neural networks, for example, may be developed based upon empiricaldata using advanced regression algorithms.

The analytics module 250 may further include an optimizer 254. As willbe appreciated, an optimizer may be used to minimize a “cost function”subject to a set of constraints, where the cost function is amathematical representation of desired objectives or system operation.Because the models 252 may be non-linear, the optimizer 254 may be anonlinear programming optimizer. It is contemplated, however, that thepresent invention may be implemented by using, individually or incombination, a variety of different types of optimization approaches,including, but not limited to, linear programming, quadraticprogramming, mixed integer non-linear programming, stochasticprogramming, global non-linear programming, genetic algorithms,particle/swarm techniques, and the like.

According to exemplary embodiments, the models 252 and the optimizer 254may together be used within an optimization system 255. For example, theanalytics module 250 may utilize the optimization system 255 as part ofan optimization process by which aspects of contact center performanceand operation are optimized or, at least, enhanced. This, for example,may include aspects related to the customer experience, agentexperience, interaction routing, natural language processing, intentrecognition, or other functionality related to automated processes.

The various components, modules, and/or servers of FIG. 2 (as well asthe other figures included herein) may each include one or moreprocessors executing computer program instructions and interacting withother system components for performing the various functionalitiesdescribed herein. Such computer program instructions may be stored in amemory implemented using a standard memory device, such as, for example,a random-access memory (RAM), or stored in other non-transitory computerreadable media such as, for example, a CD-ROM, flash drive, etc.Although the functionality of each of the servers is described as beingprovided by the particular server, a person of skill in the art shouldrecognize that the functionality of various servers may be combined orintegrated into a single server, or the functionality of a particularserver may be distributed across one or more other servers withoutdeparting from the scope of the present invention. Further, the terms“interaction” and “communication” are used interchangeably, andgenerally refer to any real-time and non-real-time interaction that usesany communication channel including, without limitation, telephone calls(PSTN or VoIP calls), emails, vmails, video, chat, screen-sharing, textmessages, social media messages, WebRTC calls, etc. Access to andcontrol of the components of the contact system 200 may be affectedthrough user interfaces (UIs) which may be generated on the customerdevices 205 and/or the agent devices 230. As already noted, the contactcenter system 200 may operate as a hybrid system in which some or allcomponents are hosted remotely, such as in a cloud-based or cloudcomputing environment.

Chat Systems

Turning to FIGS. 3, 4 and 5, various aspects of chat systems andchatbots are shown. As will be seen, present embodiments may include orbe enabled by such chat features, which, in general, enable the exchangeof text messages between different parties. Those parties may includelive persons, such as customers and agents, as well as automatedprocesses, such as bots or chatbots.

By way of background, a bot (also known as an “Internet bot”) is asoftware application that runs automated tasks or scripts over theInternet. Typically, bots perform tasks that are both simple andstructurally repetitive at a much higher rate than would be possible fora person. A chatbot is a particular type of bot and, as used herein, isdefined as a piece of software and/or hardware that conducts aconversation via auditory or textual methods. As will be appreciated,chatbots are often designed to convincingly simulate how a human wouldbehave as a conversational partner. Chatbots are typically used indialog systems for various practical purposes including customer serviceor information acquisition. Some chatbots use sophisticated naturallanguage processing systems, while simpler ones scan for keywords withinthe input and then select a reply from a database based on matchingkeywords or wording pattern.

Before proceeding further with the description of the present invention,an explanatory note will be provided in regard to referencing systemcomponents—e.g., modules, servers, and other components—that havealready been introduced in any previous figure. Whether or not thesubsequent reference includes the corresponding numerical identifiersused in the previous figures, it should be understood that the referenceincorporates the example described in the previous figures and, unlessotherwise specifically limited, may be implemented in accordance witheither that examples or other conventional technology capable offulfilling the desired functionality, as would be understood by one ofordinary skill in the art. Thus, for example, subsequent mention of a“contact center system” should be understood as referring to theexemplary “contact center system 200” of FIG. 2 and/or otherconventional technologies for implementing a contact center system. Asadditional examples, a subsequent mention below to a “customer device”,“agent device”, “chat server”, or “computing device” should beunderstood as referring to the exemplary “customer device 205”, “agentdevice 230”, “chat server 240”, or “computing device 200”, respectively,of FIGS. 1-2, as well as conventional technology for fulfilling the samefunctionality.

Chat features and chatbots will now be discussed in greater specificitywith reference to the exemplary embodiments of a chat server, chatbot,and chat interface depicted, respectively, in FIGS. 3, 4, and 5. Whilethese examples are provided with respect to chat systems implemented onthe contact center-side, such chat systems may be used on thecustomer-side of an interaction. Thus, it should be understood that theexemplary chat systems of FIGS. 3, 4, and 5 may be modified foranalogous customer-side implementation, including the use ofcustomer-side chatbots configured to interact with agents and chatbotsof contact centers on a customer's behalf. It should further beunderstood that chat features may be utilized by voice communicationsvia converting text-to-speech and/or speech-to-text.

Referring specifically now to FIG. 3, a more detailed block diagram isprovided of a chat server 240, which may be used to implement chatsystems and features. The chat server 240 may be coupled to (i.e., inelectronic communication with) a customer device 205 operated by thecustomer over a data communications network 210. The chat server 240,for example, may be operated by a enterprise as part of a contact centerfor implementing and orchestrating chat conversations with thecustomers, including both automated chats and chats with human agents.In regard to automated chats, the chat server 240 may host chatautomation modules or chatbots 260A-260C (collectively referenced as260), which are configured with computer program instructions forengaging in chat conversations. Thus, generally, the chat server 240implements chat functionality, including the exchange of text-based orchat communications between a customer device 205 and an agent device230 or a chatbot 260. As discussed more below, the chat server 240 mayinclude a customer interface module 265 and agent interface module 266for generating particular UIs at the customer device 205 and the agentdevice 230, respectively, that facilitate chat functionality.

In regard to the chatbots 260, each can operate as an executable programthat is launched according to demand. For example, the chat server 240may operate as an execution engine for the chatbots 260, analogous toloading VoiceXML files to a media server for interactive voice response(IVR) functionality. Loading and unloading may be controlled by the chatserver 240, analogous to how a VoiceXML script may be controlled in thecontext of an interactive voice response. The chat server 240 mayfurther provide a means for capturing and collecting customer data in aunified way, similar to customer data capturing in the context of IVR.Such data can be stored, shared, and utilized in a subsequentconversation, whether with the same chatbot, a different chatbot, anagent chat, or even a different media type. In example embodiments, thechat server 240 is configured to orchestrate the sharing of data amongthe various chatbots 260 as interactions are transferred or transitionedover from one chatbot to another or from one chatbot to a human agent.The data captured during interaction with a particular chatbot may betransferred along with a request to invoke a second chatbot or humanagent.

In exemplary embodiments, the number of chatbots 260 may vary accordingto the design and function of the chat server 240 and is not limited tothe number illustrated in FIG. 3. Further, different chatbots may becreated to have different profiles, which can then be selected betweento match the subject matter of a particular chat or a particularcustomer. For example, the profile of a particular chatbot may includeexpertise for helping a customer on a particular subject orcommunication style aimed at a certain customer preference. Morespecifically, one chatbot may be designed to engage in a first topic ofcommunication (e.g., opening a new account with the business), whileanother chatbot may be designed to engage in a second topic ofcommunication (e.g., technical support for a product or service providedby the business). Or, chatbots may be configured to utilize differentdialects or slang or have different personality traits orcharacteristics. Engaging chatbots with profiles that are catered tospecific types of customers may enable more effective communication andresults. The chatbot profiles may be selected based on information knownabout the other party, such as demographic information, interactionhistory, or data available on social media. The chat server 240 may hosta default chatbot that is invoked if there is insufficient informationabout the customer to invoke a more specialized chatbot. Optionally, thedifferent chatbots may be customer selectable. In exemplary embodiments,profiles of chatbots 260 may be stored in a profile database hosted inthe storage device 220. Such profiles may include the chatbot'spersonality, demographics, areas of expertise, and the like.

The customer interface module 265 and agent interface module 266 may beconfigured to generating user interfaces (UIs) for display on thecustomer device 205 that facilitate chat communications between thecustomer and a chatbot 260 or human agent. Likewise, an agent interfacemodule 266 may generate particular UIs on the agent device 230 thatfacilitate chat communications between an agent operating an agentdevice 230 and the customer. The agent interface module 266 may alsogenerate UIs on an agent device 230 that allow an agent to monitoraspects of an ongoing chat between a chatbot 260 and a customer. Forexample, the customer interface module 265 may transmit signals to thecustomer device 205 during a chat session that are configured togenerated particular UIs on the customer device 205, which may includethe display of the text messages being sent from the chatbot 260 orhuman agent as well as other non-text graphics that are intended toaccompany the text messages, such as emoticons or animations. Similarly,the agent interface module 266 may transmit signals to the agent device230 during a chat session that are configured to generated UIs on theagent device 230. Such UIs may include an interface that facilitates theagent selection of non-text graphics for accompanying outgoing textmessages to customers.

In exemplary embodiments, the chat server 240 may be implemented in alayered architecture, with a media layer, a media control layer, and thechatbots executed by way of the IMR server 216 (similar to executing aVoiceXML on an IVR media server). As described above, the chat server240 may be configured to interact with the knowledge management server234 to query the server for knowledge information. The query, forexample, may be based on a question received from the customer during achat. Responses received from the knowledge management server 234 maythen be provided to the customer as part of a chat response.

Referring specifically now to FIG. 4, a block diagram is provided of anexemplary chat automation module or chatbot 260. As illustrated, thechatbot 260 may include several modules, including a text analyticsmodule 270, dialog manager 272, and output generator 274. It will beappreciated that, in a more detailed discussion of chatbot operability,other subsystems or modules may be described, including, for examples,modules related to intent recognition, text-to-speech or speech-to-textmodules, as well as modules related to script storage, retrieval, anddata field processing in accordance with information stored in agent orcustomer profiles. Such topics, however, are covered more completely inother areas of this disclosure—for example, in relation to FIGS. 6 and7—and so will not be repeated here. It should nevertheless be understoodthat the disclosures made in these areas may be used in analogous waystoward chatbot operability in accordance with functionality describedherein.

The text analytics module 270 may be configured to analyze andunderstand natural language. In this regard, the text analytics modulemay be configured with a lexicon of the language, syntactic/semanticparser, and grammar rules for breaking a phrase provided by the customerdevice 205 into an internal syntactic and semantic representation. Theconfiguration of the text analytics module depends on the particularprofile associated with the chatbot. For example, certain words may beincluded in the lexicon for one chatbot but excluded that of another.

The dialog manager 272 receives the syntactic and semanticrepresentation from the text analytics module 270 and manages thegeneral flow of the conversation based on a set of decision rules. Inthis regard, the dialog manager 272 maintains a history and state of theconversation and, based on those, generates an outbound communication.The communication may follow the script of a particular conversationpath selected by the dialog manager 272. As described in further detailbelow, the conversation path may be selected based on an understandingof a particular purpose or topic of the conversation. The script for theconversation path may be generated using any of various languages andframeworks conventional in the art, such as, for example, artificialintelligence markup language (AIML), SCXML, or the like.

During the chat conversation, the dialog manager 272 selects a responsedeemed to be appropriate at the particular point of the conversationflow/script and outputs the response to the output generator 274. Inexemplary embodiments, the dialog manager 272 may also be configured tocompute a confidence level for the selected response and provide theconfidence level to the agent device 230. Every segment, step, or inputin a chat communication may have a corresponding list of possibleresponses. Responses may be categorized based on topics (determinedusing a suitable text analytics and topic detection scheme) andsuggested next actions are assigned. Actions may include, for example,responses with answers, additional questions, transfer to a human agentto assist, and the like. The confidence level may be utilized to assistthe system with deciding whether the detection, analysis, and responseto the customer input is appropriate or whether a human agent should beinvolved. For example, a threshold confidence level may be assigned toinvoke human agent intervention based on one or more business rules. Inexemplary embodiments, confidence level may be determined based oncustomer feedback. As described, the response selected by the dialogmanager 272 may include information provided by the knowledge managementserver 234.

In exemplary embodiments, the output generator 274 takes the semanticrepresentation of the response provided by the dialog manager 272, mapsthe response to a chatbot profile or personality (e.g., by adjusting thelanguage of the response according to the dialect, vocabulary, orpersonality of the chatbot), and outputs an output text to be displayedat the customer device 205. The output text may be intentionallypresented such that the customer interacting with a chatbot is unawarethat it is interacting with an automated process as opposed to a humanagent. As will be seen, in accordance with other embodiments, the outputtext may be linked with visual representations, such as emoticons oranimations, integrated into the customer's user interface.

Reference will now be made to FIG. 5, in which a webpage 280 having anexemplary implementation of a chat feature 282 is presented. The webpage280, for example, may be associated with an enterprise website andintended to initiate interaction between prospective or currentcustomers visiting the webpage and a contact center associated with theenterprise. As will be appreciated, the chat feature 282 may begenerated on any type of customer device 205, including personalcomputing devices such as laptops, tablet devices, or smart phones.Further, the chat feature 282 may be generated as a window within awebpage or implemented as a full-screen interface. As in the exampleshown, the chat feature 282 may be contained within a defined portion ofthe webpage 280 and, for example, may be implemented as a widget via thesystems and components described above and/or any other conventionalmeans. In general, the chat feature 282 may include an exemplary way forcustomers to enter text messages for delivery to a contact center.

As an example, the webpage 280 may be accessed by a customer via acustomer device, such as the customer device, which provides acommunication channel for chatting with chatbots or live agents. Inexemplary embodiments, as shown, the chat feature 282 includesgenerating a user interface, which is referred to herein as a customerchat interface 284, on a display of the customer device. The customerchat interface 284, for example, may be generated by the customerinterface module of a chat server, such as the chat server, as alreadydescribed. As described, the customer interface module 265 may sendsignals to the customer device 205 that are configured to generate thedesired customer chat interface 284, for example, in accordance with thecontent of a chat message issued by a chat source, which, in theexample, is a chatbot or agent named “Kate”. The customer chat interface284 may be contained within a designated area or window, with thatwindow covering a designated portion of the webpage 280. The customerchat interface 284 also may include a text display area 286, which isthe area dedicated to the chronological display of received and senttext messages. The customer chat interface 284 further includes a textinput area 288, which is the designated area in which the customerinputs the text of their next message. As will be appreciated, otherconfigurations are also possible.

Customer Automation Systems

Embodiments of the present invention include systems and methods forautomating and augmenting customer actions during various stages ofinteraction with a customer service provider or contact center. As willbe seen, those various stages of interaction may be classified aspre-contact, during-contact, and post-contact stages (or, respectively,pre-interaction, during-interaction, and post-interaction stages). Withspecific reference now to FIG. 6, an exemplary customer automationsystem 300 is shown that may be used with embodiments of the presentinvention. To better explain how the customer automation system 300functions, reference will also be made to FIG. 7, which provides aflowchart 350 of an exemplary method for automating customer actionswhen, for example, the customer interacts with a contact center.Additional information related to customer automation are provided inU.S. application Ser. No. 16/151,362, filed on Oct. 4, 2018, entitled“System and Method for Customer Experience Automation”, the content ofwhich is incorporated herein by reference.

The customer automation system 300 of FIG. 6 represents a system thatmay be generally used for customer-side automations, which, as usedherein, refers to the automation of actions taken on behalf of acustomer in interactions with customer service providers or contactcenters. Such interactions may also be referred to as “customer-contactcenter interactions” or simply “customer interactions”. Further, indiscussing such customer-contact center interactions, it should beappreciated that reference to a “contact center” or “customer serviceprovider” is intended to generally refer to any customer servicedepartment or other service provider associated with an organization orenterprise (such as, for example, a business, governmental agency,non-profit, school, etc.) with which a user or customer has business,transactions, affairs or other interests.

In exemplary embodiments, the customer automation system 300 may beimplemented as a software program or application running on a mobiledevice or other computing device, cloud computing devices (e.g.,computer servers connected to the customer device 205 over a network),or combinations thereof (e.g., some modules of the system areimplemented in the local application while other modules are implementedin the cloud. For the sake of convenience, embodiments are primarilydescribed in the context of implementation via an application running onthe customer device 205. However, it should be understood that presentembodiments are not limited thereto.

The customer automation system 300 may include several components ormodules. In the illustrated example of FIG. 6, the customer automationsystem 300 includes a user interface 305, natural language processing(NLP) module 310, intent inference module 315, script storage module320, script processing module 325, customer profile database or module(or simply “customer profile”) 330, communication manager module 335,text-to-speech module 340, speech-to-text module 342, and applicationprogramming interface (API) 345, each of which will be described withmore particularity with reference also to flowchart 350 of FIG. 7. Itwill be appreciated that some of the components of and functionalitiesassociated with the customer automations system 300 may overlap with thechatbot systems described above in relation to FIGS. 3, 4, and 5. Incases where the customer automation system 300 and such chatbot systemsare employed together as part of a customer-side implementation, suchoverlap may include the sharing of resources between the two systems.

In an example of operation, with specific reference now to the flowchart350 of FIG. 7, the customer automation system 300 may receive input atan initial step or operation 355. Such input may come from severalsources. For example, a primary source of input may be the customer,where such input is received via the customer device. The input also mayinclude data received from other parties, particularly partiesinteracting with the customer through the customer device. For example,information or communications sent to the customer from the contactcenter may provide aspects of the input. In either case, the input maybe provided in the form of free speech or text (e.g., unstructured,natural language input). Input also may include other forms of datareceived or stored on the customer device.

Continuing with the flow diagram 350, at an operation 360, the customerautomation system 300 parses the natural language of the input using theNLP module 310 and, therefrom, infers an intent using the intentinference module 315. For example, where the input is provided as speechfrom the customer, the speech may be transcribed into text by aspeech-to-text system (such as a large vocabulary continuous speechrecognition or LVCSR system) as part of the parsing by the NLP module310. The transcription may be performed locally on the customer device205 or the speech may be transmitted over a network for conversion totext by a cloud-based server. In certain embodiments, for example, theintent inference module 315 may automatically infer the customer'sintent from the text of the provided input using artificial intelligenceor machine learning techniques. Such artificial intelligence techniquesmay include, for example, identifying one or more keywords from thecustomer input and searching a database of potential intentscorresponding to the given keywords. The database of potential intentsand the keywords corresponding to the intents may be automatically minedfrom a collection of historical interaction recordings. In cases wherethe customer automation system 300 fails to understand the intent fromthe input, a selection of several intents may be provided to thecustomer in the user interface 305. The customer may then clarify theirintent by selecting one of the alternatives or may request that otheralternatives be provided.

After the customer's intent is determined, the flowchart 350 proceeds toan operation 365 where the customer automation system 300 loads a scriptassociated with the given intent. Such scripts, for example, may bestored and retrieved from the script storage module 320. Such scriptsmay include a set of commands or operations, pre-written speech or text,and/or fields of parameters or data (also “data fields”), whichrepresent data that is required to automate an action for the customer.For example, the script may include commands, text, and data fields thatwill be needed in order to resolve the issue specified by the customer'sintent. Scripts may be specific to a particular contact center andtailored to resolve particular issues. Scripts may be organized in anumber of ways, for example, in a hierarchical fashion, such as whereall scripts pertaining to a particular organization are derived from acommon “parent” script that defines common features. The scripts may beproduced via mining data, actions, and dialogue from previous customerinteractions. Specifically, the sequences of statements made during arequest for resolution of a particular issue may be automatically minedfrom a collection of historical interactions between customers andcustomer service providers. Systems and methods may be employed forautomatically mining effective sequences of statements and comments, asdescribed from the contact center agent side, are described in U.S.patent application Ser. No. 14/153,049 “Computing Suggested Actions inCaller Agent Phone Calls By Using Real-Time Speech Analytics andReal-Time Desktop Analytics,” filed in the United States Patent andTrademark Office on Jan. 12, 2014, the entire disclosure of which isincorporated by reference herein.

With the script retrieved, the flowchart 350 proceeds to an operation370 where the customer automation system 300 processes or “loads” thescript. This action may be performed by the script processing module325, which performs it by filling in the data fields of the script withappropriate data pertaining to the customer. More specifically, thescript processing module 325 may extract customer data that is relevantto the anticipated interaction, with that relevance being predeterminedby the script selected as corresponding to the customer's intent. Thedata for many of the data fields within the script may be automaticallyloaded with data retrieved from data stored within the customer profile330. As will be appreciated, the customer profile 330 may storeparticular data related to the customer, for example, the customer'sname, birth date, address, account numbers, authentication information,and other types of information relevant to customer serviceinteractions. The data selected for storage within the customer profile330 may be based on data the customer has used in previous interactionsand/or include data values obtained directly by the customer. In case ofany ambiguity regarding the data fields or missing information within ascript, the script processing module 325 may include functionality thatprompts and allows the customer to manually input the neededinformation.

Referring again to the flowchart 350, at an operation 375, the loadedscript may be transmitted to the customer service provider or contactcenter. As discussed more below, the loaded script may include commandsand customer data necessary to automate at least a part of aninteraction with the contact center on the customer's behalf. Inexemplary embodiments, an API 345 is used so to interact with thecontact center directly. Contact centers may define a protocol formaking commonplace requests to their systems, which the API 345 isconfigured to do. Such APIs may be implemented over a variety ofstandard protocols such as Simple Object Access Protocol (SOAP) usingExtensible Markup Language (XML), a Representational State Transfer(REST) API with messages formatted using XML or JavaScript ObjectNotation (JSON), and the like. Accordingly, the customer automationsystem 300 may automatically generate a formatted message in accordancewith a defined protocol for communication with a contact center, wherethe message contains the information specified by the script inappropriate portions of the formatted message.

Bot Authoring Using Intent Mining Automation

With several breakthroughs in Artificial Intelligence (AI) and computingtechnologies in recent years, there has been an increased interest inapplications, automated systems, chat bots or bots that can engage innatural language conversations with humans. Recent years have witnesseda tremendous growth in the adoption of AI-powered chatbots and virtualassistants that can converse with humans naturally and perform a widevariety of tasks in a self-service fashion. Such conversational botswork by first analyzing a user's input and then trying to understand themeaning of that input. This is referred to as Natural LanguageUnderstanding (or “NLU”) and typically involves the identification of auser's intention or “intent” and certain key words or “entities” in theuser's input utterance. Once the intent and entities are determined, abot can respond to a user with an appropriate follow-up action.

Various machine learning algorithms are used to train NLU models.Training typically involves teaching the system to recognize patternspresent in natural language inputs and associate them with a pre-definedset of intents. The quality of training data is a critical factor indetermining model performance. A sufficiently large data set, withadequate diversity in input utterances, is crucial for building good NLUmodels.

As used herein, the term “bot authoring” refers to the process ofcreating a conversational bot or chatbot with NLU capabilities. Thisprocess generally involves defining intents, identifying entities,formulating utterances, training NLU models, testing the bot and finallypublishing it. This is usually a mostly manual process which may takeweeks or months to complete. Generally, identifying intents andformulating utterances take most of this time. Although organizationsmay already possess large amounts of chat conversations between theircustomers and customer support staff, such as contact center agents, theprocess of manually going through these raw chat transcripts to identifyintents and utterances cost both time and money.

As used herein, an intent mining engine or process (which may bereferenced generally as a “intent mining process”) is a system or methodthat makes the bot authoring workflow more efficient. As will be seen,the intent mining process of the present invention functions by miningintents from tens of thousands of conversations and finds a robust anddiverse set of utterances belonging to each one. Further, the intentmining process helps to gain insights into the conversations byproviding conversational analytics. It also provides the bot author withan opportunity to analyze intents and make modifications. Finally, theseintents and utterances may be exported to diverse chatbot authoringplatforms such as those commercially available in Genesys Dialog Engine,Google's Dialogflow, and Amazon Lex. As will be seen, this results in aflexible and efficient bot authoring workflow that significantly reducesoverall development time.

With reference now to FIG. 8, various stages or steps of bot authoringworkflow 400 are shown using the intent mining process of the presentinvention (or simply “present intent mining process”). To initiate theworkflow 400, conversations or conversation data may be imported formining. Such conversations data may consist of previously occurringinteractions between agents and customers. Such conversation data may benatural language conversations consisting of multiple back and forthmessaging turns. The conversations, for example, may have occurred via achat interface, through text, or via voice calls. In the case of thelatter, the conversations may be transcribed into text via speechrecognition before the mining begins.

At an initial step 405, the bot authoring workflow 400 may includeimporting conversation data (i.e., conversational text data) for use inthe intent mining process. This may be done in several ways. Forexample, the conversational data may be imported via a text file (in asupported format like JSON) containing the conversations to be mined.The conversational data also may be imported from cloud storage.

At a step 410, the bot authoring workflow 400 may include mining theintents from the conversational data. As discussed in relation to FIGS.9 and 10 below, the intents may be mined in accordance with an intentmining algorithm.

At a step 415, the bot authoring workflow 400 may include testing themined intents. This may include interacting with the output of theintent mining process. That is, at this stage of the workflow, the botauthor interacts with the mined output to make edits, which may includefine-tuning and pruning intents and associated utterances beforeexporting them into a bot for training. The bot author may performvarious actions on the mined output, such as, for example: selecting anintent and the utterances that belong to that intent; merging two ormore intents into a single intent, which may result in the merger oftheir chosen utterances; split an intent into multiple intents, whichresults in the splitting of corresponding utterances; and renamingintent labels. At the end of this business logic-driven process, amodified set of intents and associated utterances are produced that maythen be used to train a chatbot.

At a step 420, the bot authoring workflow 400 may include importing themined intents and utterances into the bot. For example, the minedintents may be uploaded into the conversational bot, and theconversational bot may be used to conduct automated conversations withcustomers. The present intent mining process may provide multiple waysto add mined or modified intents and utterances to bots. The data may bedownloaded in CSV format for convenient review. The data can also beexported to multiple bot formats, thus providing support to a widervariety of conversational AI chatbot services, such as Genesys DialogEngine, Google's Dialogflow or Amazon Lex.

The bot authoring process may also include additional steps. Accordingto certain embodiments, the present intent mining process may besignificantly involved in the steps already described above and lessinvolved in later developmental stages. These later steps may include anoptional editing step, a bot design step, and, finally, a final testingand publishing step.

With reference now to FIG. 9, an exemplary algorithm for implementingthe present intent mining engine or process 500 will now be discussed.As will be seen, this algorithm may be approximately broken down intoseveral steps, with will be referred to herein as: 1) identifyingintent-bearing utterances; 2) generating candidate intents; 3)identifying salient intents; 4) semantic grouping of intents; 5) intentlabeling; and 6) utterance-intent association. Other steps may includethe masking of personally identifiable information in utterances.Another additional step may include the computation of intent analytics.These steps will now be discussed. As will be seen, the steps will bedescribed in relation to imported conversation data, for example, datathat includes natural language conversations between customersinteracting with customer service representatives or agents, though itshould be appreciated that the process also may be applicable to othercontexts as well involving other types of users and conversation types.

In accordance with a first step 505, the present intent mining processprocesses the conversation data to identify intent-bearing turns orutterances. As used herein, intent-bearing utterances are thoseutterances that are determined to likely include or describe an intentof the customer. Thus, this initial step in the present intent miningprocess is to identify the intent-bearing utterances from the givenconversations. For example, a conversation typically consists ofmultiple message turns or utterances from multiple parties such as anagent (which may include an automated system or bot or human agent) anda customer.

As an example, a bot-generated message might look like this: “Hello,thank you for contacting us. All chats may be monitored or recorded forquality and training purposes. We will be with you shortly to help youwith your request”. Such bot-generated messages can be safely discardedas they tend to be generic and throw no light into intents found in aconversation. The actual conversation begins with either the agent orcustomer sending a substantive communication or message. For example,during an interaction, a customer may explain the reason or the “intent”for contacting the customer care. Subsequent agent-customerconversational turns take place based on this intent expressed by thecustomer.

From the analysis of real-world customer-agent conversations, thepresent invention includes several heuristics or strategies foridentifying intent-bearing utterances. For example, it has been observedthat intent-bearing turns typically occurs towards the beginning of thecustomer side of the conversation. Hence, only a few of the initialcustomer utterances generally need to be processed to identify theintent, and the rest of the conversation can be discarded. This furtherhelps in reducing the latency and memory footprints of the system.Further, word-count constraints may be used to discard other utterancesas being unlikely to include a customer intent.

As an example, identification of intent-bearing utterances may includethe following. A set of consecutive customer utterances in theconversation is selected. This set may include the customer utterancesoccurring within the beginning of the conversation. Additionally, aword-count constraint may be used to disqualify some of the customerutterances within this initial set. That is, to qualify, the number ofwords in each turn must be greater than a minimum threshold. Such aword-count or length constraint helps to discard some customer turnsthat are irrelevant for intent mining purposes, such as customarygreetings like “Hello”, “Hi there”, “How are you?”, etc. For example,this minimum word-count threshold may be set at between 2 and 5.

The present intent mining process may concatenate the utterances fromthe consecutive customer turns of the intent-bearing turns into a singlecombined utterance. Before this is done, each of the customer turns maybe pruned based on a maximum length threshold, as longer sentences tendto not to be coherent or produce noisy results. As an example, themaximum number of words per utterance may be set at 50 words. Thus, atthe end of this step, a combined utterance is obtained from eachconversation that likely contains the intent expressed by the customer.If a conversation does not contain message turns that meet the abovecriteria, it may be discarded without obtaining a combined utterancefrom it. Since the present intent mining process is used to obtain thedominant intents from several hundreds or even thousands ofconversations, it may be safely assumed that customer intents arerepeated across multiple conversations. Hence, the conversations thatfail to meet the above heuristic criteria might be discarded withoutaffecting the system's functionality for the sake of greater robustnessin intent identification.

In accordance with a second step 510, candidate intents are generatedbased on analysis of the combined utterance. That is, once theutterances from the intent-bearing turns are obtained from conversationand combined, the next task includes identifying the possible or likelyintents, which will be referred to herein as “candidate intents”. Asused herein, a candidate intent is a text phrase consisting of twoparts: 1) an action, which is a word or a phrase representing a tangiblepurpose, task or activity, and 2) an object, which represents thosewords or phrases that the action is going to act or operate upon.

There are different ways to obtain these action-object pairs fromutterances. As will be appreciated, the choice may depend on thelinguistic model and resources available for a particular language.Typically, for example, a syntactic dependency parser is used to analyzethe grammatical structure of an utterance and obtain the relationshipsbetween “head” words and “tokens” or the words which modify those heads.These relationships between the tokens of an utterance and their heads,along with their Part-of-Speech (POS) tags, are used to identify thepotential or candidate intents for a given utterance.

As an example, the process of obtaining such action-object pairs mayinclude the followings. First, all token and head pairs in an utterancemay be obtained using a dependency parser. From those, pairs areselected with the POS tags of the token and its associated head beingNOUN and VERB, respectively. The usage of universal POS tags helps tomake the system language agnostic and hence expandable to multiplelinguistic domains.

The “action” part is usually the token having “verb” as the associatedPOS tag. If the token is a “base verb” with a “particle” token, then thetoken forms a “phrasal verb” of an utterance. The associated “particle”token is also included with the verb token. Thus, the entire phrasalverb becomes the action part of the candidate intent. The “object” partis usually the token with “noun” as the associated POS tag. If the tokenis part of a “compound” with all the constituent tokens having a “noun”POS tag, then the whole compound is taken as the object. Similarly, ifthe token is part of an adjectival modifier phrase, then the wholephrase is taken as the object. If the token is associated with anappositional modifier, then all the tokens constituting the latter areappended to the current token to form the object part of the candidateintent. If only the universal POS tags are available for a language andnot the universal dependencies, then the “verb” and “noun” tokens aretaken as the action and object parts, respectively. As a next step, theaction-object ordered pairs may be lemmatized to convert the candidateintents into a more standard form. For further normalization, the caseof the lemmatized pairs may be lowered.

Thus, one or more normalized action-object pairs may be obtained fromeach utterance, which together form the candidate intents of theconversations. If no such pair is obtained, that utterance is discarded.With this in mind, consider a first exemplary utterance: “I'm looking tocontact the instructor for this course. Can you provide his emailplease?” In this case, candidate intents may include “contactinstructor” and “provide email”. Consider a second exemplary utterance:“I just finished my bachelor's program yesterday on my account it saysyou must complete a graduation application, but when I click it goes toa page that says messages and only shows potential scholarships whatshould I do?” In this case, candidate intents may include “finishprogram”, “complete graduation application”, “say message”, and “showpotential scholarship”.

In accordance with a third step 515, salient intents are identified. Asused herein, the term “salient intents” refers to a narrowed list ofintents from the candidate intents identified in the previous step,where that narrowing is based on, for example, relevance, significance,definitiveness, and/or noticeability. Thus, from the set of candidateintents, those intents that describe the customers' actual intentionsare identified as salient intents. As will be appreciated, this task isnot always straightforward. In some cases, the intention of the customermay be implicit in nature. In others, however, there might differingopinions regarding the actual intention of customer, especially in thoseutterances which contain multiple candidate intents.

Consider the examples provided above. In the case of the first utteranceexample, it may be argued that both “contact instructor” and “provideemail” describe the intention of the customer. And, in the case ofsecond utterance example, the customer has finished his/her bachelor'sprogram and is facing an issue while completing the graduationapplication. While this intention is more implicit, the closest explicitapproximation could be the candidate intent “complete graduationapplication”. The decision whether “contact instructor” or “provideemail” should be chosen as the intent of the first utterance, or evenwhether “finish program” or “complete graduation application” should bechosen as the intent of the second utterance, might be better determinedby business logic than by any algorithmic formulation. That is, the botauthor might apply the appropriate business logic to reach a finaldecision on such intents. The bot author may also choose to retainmultiple intents or even describe a hierarchy of intents to achieve theappropriate business objectives or goals within a particular businessdomain.

As the aim is make the bot authoring process more efficient, the presentintent mining process may narrow down the list of candidate intents intothe most salient ones, which then the bot author may review forappropriateness. In such cases, salience may be defined in multiple waysbased on different criteria. For example, according to exemplaryembodiments, the frequency of candidate intents in the whole set ofutterances could be an indicator of salience, i.e., the higher thenumber of a candidate intent, the higher the relevance. In accordancewith other embodiments of the present invention, a criterion based onLatent Semantic Analysis (LSA) may be used to find the salient intents.LSA is a topic modelling technique used in Natural LanguageUnderstanding (NLU) tasks. To do this, each utterance, described interms of candidate intent action-object pairs, is considered as adocument. LSA then analyzes the relationship between these documents andthe terms they contain (i.e., the action-object pairs) by producing aset of concepts related to the documents and those included terms. Eachconcept is described in terms of candidate intents with associatedweights. These weights offer insights into the relative prominence ofcandidate intents within each conceptual group.

As an example, in accordance with the present invention, the process ofidentifying salient intents may include the following. First, LSA isapplied to utterances described in terms of candidate intentaction-object pairs with the number of LSA components being set to apredetermined limit, for example, 50. The candidate intents of eachconceptual group are then sorted in descending order in relation totheir weights and the top candidate intents, for example, the top 5, areselected. The selected candidate intents obtained from each conceptualgroup are then collated and arranged in descending order in relation totheir weights. Duplicate entries are then discarded, with the entryhaving the higher weight being kept. A predetermined number of these maythen be deemed the salient candidate intents or simply “salientintents”. The predetermined number may be based on the maximum number ofintents that need to be mined. For example, this maximum number ofintents may be determined by the present intent mining process based onreal-world contact center interaction patterns or be chosen by the botauthor based on appropriate business logic and use cases.

In accordance with a fourth step 520, the salient intents aresemantically grouped. As will be appreciate, since only the syntacticstructure of utterances is used to generate candidate intents, it ispossible that many of the salient intents identified by the system aresimilar in meaning. Semantically similar salient intents, thus, may begrouped together for optimum downstream functionality. The output of thepresent intent mining process might be used to train Natural LanguageUnderstanding (NLU) models which then effectively form the “brain” of anatural language chatbot. For these models to identify intentsassociated with diverse utterances, the NLU model must be trained bysyntactically different, but semantically similar utterances. Hence, thebot authoring process must enable the creation of intents beingassociated with utterances having adequate diversity. The grouping ofsemantically similar salient intents helps to produce this diversity inthe mined intents.

This step generally includes calculating a semantic similarity betweenthe salient intents, which, as an example, may be completed as follows.First, embeddings or word-embeddings associated with the text of thesalient intents are computed. As will be appreciated, such embeddingsrepresent the subject text, e.g., a word, phrase, or sentence, such thatsemantically similar texts have similar embeddings. Such word-embeddingsgenerally include converting the text data into a numeric format via anencoding process, and various conventional encoding techniques may beused to extract such word-embeddings from the text data. The embeddingscan then be efficiently compared to determine a measure of semanticsimilarity between the texts. As an example, Global Vectors (or “GloVe”)is an algorithm that may be used to obtain vector representations forwords. A GloVe model, for example, may have 300 dimensions. In exampleembodiments, the word-embeddings for the salient intents may be computedusing Inverse Document Frequency (IDF)-weighted average of GloVeembeddings of the constituent tokens. As will be appreciated, IDF is anumerical statistic reflecting a measure as to whether a term is commonor rare in a given document corpus. Used in this manner, the collectionof all candidate intents or salient intents can be considered as thedocument corpus for the purpose of IDF computation here.

Once the word-embeddings for the text of the salient intents isobtained, the word-embeddings may be used to calculate a semanticsimilarity between pairs of the salient intents. As an example, cosinesimilarity can be used to provide a measure of semantic closenessbetween word-embeddings in the higher dimensional space. With thisobtained, the salient intents can then be group in accordance to thosepairs having a cosine similarity of embeddings greater than apredetermined similarity threshold, which may be set between a range of0 and 1. As will be appreciated, the higher this threshold is, the lesssalient intents get grouped together, thereby producing groups that aremore homogenous, whereas a lower threshold value would result in moresemantically diverse intents being grouped together, producing a lesshomogenous group. As in the case of choosing the maximum intentsmentioned above, this homogeneity value might be pre-set in the system(for example, at 0.8) chosen by the bot author. In the case of thelatter, the bot author would be able to view multiple output intents andutterance combinations and choose a value which is appropriate foroptimum bot results.

In accordance with a fifth step 525, intent labels are identified. Eachof the grouped salient intents (or “salient intent groups”) ultimatelymay be an intent that is mined (or “mined intent”). Thus, for each ofthese salient intent groups, an intent label is picked to serve as thelabel or identifier of the mined intent. According to exampleembodiments, this labeling may be done by computing the IDF of each ofthe salient intents within a given salient intent group. For thiscalculation, the utterances, described in terms of candidate intents,are taken as the documents, and the action-object pairs, taken as singleunits, are considered as the constituent tokens. The salient intent ofeach group having the highest calculated IDF is then made the intentexemplar or “intent label” for the group, while the other salientintents within the group are referred to as the “intent alternatives”.

In accordance with a sixth step 530, utterances are associated with themined intents (each of the mined intents reflected at this point by theintent labels and respective salient intent groups). As will beappreciated, this next step determines the utterances that areassociated with each of the mined intents. Like in a previous step, asemantic similarity technique using embeddings may also be employedhere. For example, semantic similarity is computed between the candidateintents derived from each of the intent-bearing utterances and each ofthe salient intents within a given salient intent group. An utterance isthen associated with that given salient intent group (which may also bereferred to as a mined intent or, simply, intent) if the similarity ofany of its constituent candidate intents is the highest with a salientintent of that salient intent group and is also determined to be above aminimum threshold (e.g., 0.8). Further, with respect each of the salientintent groups, the candidate intent of the intent bearing utterance thatproduced the highest similarity with each particular salient intentgroup may be brought into that particular salient intent group as an“intent auxiliary”. Again, a minimum threshold may also be required.Thus, within this step, a particular intent bearing utterance isassociated with one of the salient intent groups, while the constituentcandidate intents of that particular intent bearing utterance areassociated with respective salient intent groups as intent auxiliaries.Thus, each mined intent may include an intent label, as previouslydescribed, as well as one or more intent alternatives and/or one or moreintent auxiliaries. As will be appreciated, such a formulation does notprevent the possibility of single intent-bearing utterance becomingassociated with multiple intent groups. This is because a singleintent-bearing utterance may have multiple candidate intents that areadded as intent auxiliaries to different across multiple mined intents.This introduces greater flexibility and robustness in downstreamfunctionalities. The bot author may choose to keep or discard suchutterances from one or more groups. It has been observed that utterancesrepeating across multiple intents help to teach NLU models about theinherent confusion present in them and, hence, aid in building morerealistic and robust models.

In accordance with another step (not picture), personally identifiableinformation in the utterances is removed or masked. To ensure privacy ofcustomers, all personally identifiable information that is present inthe associated utterances are masked. Of course, this step can beomitted if the input conversations are anonymized before being provideto the present intent mining process. Such personally identifiableinformation may include customer names, phone numbers, email addresses,social security, etc. In addition to this, entities related togeographical location, dates and digits may be masked as an additionalprecaution. For example, consider this utterance: “Hi, I need to book aflight from Washington D.C. to Miami on August 15 under the name of JohnHonai.” After masking, the utterance may become: “Hi, I need to book aflight from <GEO> <GEO> to <GEO> on <DATE> <DATE> under the name of<PERSON> <PERSON>.” In addition to safeguarding privacy, such maskingmay allow the bot author to quickly identify the different entitiespresent in the utterances of intents. This may help the bot authorcreate similar utterances but with varied slot values for theseentities. This leads to a greater diversity in utterances, which furtherhelps in the creation of better NLU models.

In accordance with another possible step (not pictured), intentanalytics may be computed. That is, apart from mining intents andassociated utterances, the present intent mining process also mayproduce analytics and metrics in relation to the conversation data thatassists businesses to identify customer interaction patterns. Two suchmetrics are as follows.

A first analytic is an intent volume analytic, which is an analyticregarding the extent to which conversations deal with a specific intent.This analytic may also be expressed in terms of a percentage. The intentvolume analytic may assist in understanding the relative importance ofan intent based on the frequency of its occurrence in the conversationdata. Since only a single utterance is taken from each conversation,this metric essentially becomes the number of utterances belonging toeach intent.

A second analytic is an intent duration analytic, which is an analyticregarding the duration of conversations dealing with a specific intent.This analytic may also be expressed in terms of a percentage. As will beappreciated, this metric helps to compare intents based on the totalconversational time associated with them. The time taken for aconversation is computed as the difference between the last and thefirst customer/agent turns time stamps. The sum of durations ofindividual conversations belonging to an intent gives the duration ofthat intent. As will be appreciated, this type of analytic may assistthe bot author and business to better understand customers and contactcenter staffing.

An example will now be discussed of a method for authoring aconversational bot and intent mining. The method may include: receivingconversation data, with the conversation data including text derivedfrom conversations between a customer and a customer servicerepresentative; using an intent mining algorithm to automatically mineintents from the conversation data, each of the mined intents includingan intent label, intent alternatives, and associated utterances; anduploading the mined intents into the conversational bot and using theconversational bot to conduct automated conversations with othercustomers.

In accordance with exemplary embodiments, intent mining algorithm mayinclude analyzing utterances occurring within the conversations of theconversation data to identify intent-bearing utterances. The utteranceseach may include a turn within the conversations whereby the customer,in the form of a customer utterance, or the customer servicerepresentative, in the form of a customer service representativeutterance, is communicating. And, an intent-bearing utterance is definedas one of the utterances determined to have an increased likelihood ofexpressing an intent. The intent mining algorithm may further includeanalyzing the identified intent-bearing utterances to identify candidateintents. The candidate intents may be each identified as being a textphrase occurring within one of the intent-bearing utterances that hastwo parts: an action, which may include a word or phrase describing apurpose or task, and an object, which may include a word or phrasedescribing an object or thing upon which the action operates. The intentmining algorithm may further include selecting, in accordance with oneor more criteria, salient intents from the candidate intents. The intentmining algorithm may further include grouping the selected salientintents into salient intent groups in accordance with a degree ofsemantic similarity between the salient intents. The intent miningalgorithm may further include for each of the salient intent groups,selecting one of the salient intents as the intent label and designatingthe other of the salient intents as the intent alternatives. The intentmining algorithm may further include associating the intent-bearingutterances with the salient intent groups via determining a degree ofsemantic similarity between the candidate intents present in theintent-bearing utterance and the intent alternatives within each of thesalient intent groups. The mined intents each may include a given one ofthe salient intent groups, each of which being defined by: the one ofthe salient intents that is selected as the intent label and the otherof the salient intents that are designated as the alternative intents;and the intent-bearing utterances that are associated with the given oneof the salient intent groups.

In accordance with exemplary embodiments, step of identifying theintent-bearing utterances may include selecting a first portion of thecustomer utterances as the intent-bearing utterances and discarding asecond portion of the customer utterances within the conversation data.The first portion of customer utterances may be defined as apredetermined number of consecutive customer utterances occurring at abeginning of each of the conversations, and the second portion may bedefined as the remainder of each of the conversations.

In accordance with exemplary embodiments, step of identifying theintent-bearing utterances further may include discarding the customerutterances in the first portion of customer utterances that fail tosatisfy a word-count constraint. The word-count constraint may include:a minimum word count constraint in which the customer utterances in thefirst portion of customer utterances having less words than the minimumword count constraint are discarded; and/or a maximum word countconstraint in which the customer utterances in the first portion ofcustomer utterances having more words than the maximum word countconstraint are discarded. The minimum word count constraint may includea value of between 2 and 5 words. The maximum word count constraint mayinclude a value of between 40 and 50 words.

In accordance with exemplary embodiments, step of identifyingintent-bearing utterances may include concatenating the customerutterances occurring within the first portion of each of theconversations into a combined customer utterance.

In accordance with exemplary embodiments, step of identifying candidateintents may include: using a syntactic dependency parser to analyze agrammatical structure of the intent-bearing utterance to identifyhead-token pairs, each head-token pair including a head word modified bya token word; and using parts-of-speech (hereinafter “POS”) tagging totag parts of speech of the intent-bearing utterances and identifying asthe candidate intents the head-token pairs in which the POS tag of thehead word may include a noun tag and the POS tag of the token word mayinclude a verb tag.

In accordance with exemplary embodiments, step of selecting the salientintents from the candidate intents may include selecting ones of thecandidate intents that are determined to appear more frequently in theintent-bearing utterances than other ones of the candidate intents. Theone or more criteria by which the salient intents are selected from thecandidate intents may include a criterion based on Latent SemanticAnalysis (LSA). The step of selecting the salient intents from thecandidate intents may include: generating a set of documents havingdocuments corresponding to respective ones of the candidate intents,wherein each of the documents covers an action-object pair defined bythe corresponding one of the candidate intents; generating conceptualgroups based on terms appearing in the action-object pairs contained inthe set of documents; calculating a weight value for each of thecandidate intents for each of the conceptual groups, the weight valuemeasuring a degree of relatedness between the candidate intent of agiven one of the documents and a given one of the conceptual groups; andselecting as the salient intents a predetermined number of the candidateintents in each of the conceptual groups based on which produce weightvalues indicating a higher degree of relatedness.

In accordance with exemplary embodiments, step of grouping of thesalient intents in accordance with the degree of semantic similarity mayinclude: calculating an embedding for each of the salient intents,wherein an embedding may include an encoded representation of text inwhich texts that are semantically similar have similar encodedrepresentations; comparing the calculated embeddings to determining thedegree of semantic similarity between pairs of the salient intents; andgrouping the salient intents having a degree of semantic similarityabove a predetermined threshold. The embedding may be calculated as anInverse Document Frequency (IDF) average of Global Vectors embeddings ofthe constituent head-token pairs of the salient intent. The comparingthe calculated embeddings may include cosine similarity.

In accordance with exemplary embodiments, step of labeling each of thesalient intent groups with the intent identifier may include selecting arepresentative one of the salient intents within each of the salientintent group.

In accordance with exemplary embodiments, step of associating theutterances from the conversation data with the salient intent groups mayinclude performing a first process repetitively to cover each of theintent-bearing utterances in relation to each of the salient intentgroups. If described in relation to an exemplary first case involvingfirst and second salient intent groups and a first intent-bearingutterances that contains first and second candidate intents, the firstprocess may include: computing a degree of semantic similarity betweeneach of the first and second candidate intents and each of the intentalternatives in the first salient intent group; computing a degree ofsemantic similarity between each of the first and second candidateintents and each of the intent alternatives in the second salient intentgroup; determining which of the intent alternatives produced the highestcomputed degree of semantic similarity; and associating the firstintent-bearing utterance with whichever of the first and second salientintent groups contains the intent alternative that was determined toproduce the highest computed degree of semantic similarity. The step ofassociating the utterances from the conversation data with the salientintent groups may further include associating the intent alternativeproducing the highest computed degree of semantic similarity only if thehighest computed degree of semantic similarity is also found to exceed apredetermined similarity threshold.

With reference now to FIG. 10, various stages of an alternative botauthoring workflow are shown in which the intent mining method disclosedabove in relation to FIG. 9 is augmented with a seeding of intentsprocess. The process of intent mining using an intent seeding processwill be discussed below after a brief introduction. For ease ofdistinguishing reference, the process of intent mining using intentseeding will be referred to hereafter as an “intent mining by seedingprocess” or simply “intent mining by seeding”, whereas the previouslydiscussed process of intent mining (i.e., intent mining without seeding)that was disclosed above in relation to FIG. 9, will be referred tohereafter as a “general intent mining process” or simply “general intentmining”.

In a normal mode of operation, general intent mining mines intents—bothintent labels and utterances associated therewith—from conversationdata, such as a collection of agent-customer conversations. As alreadydiscussed, this process is guided by the syntactic structure andsemantic content of the conversations. For example, syntacticdependencies and POS tags may be used to find the candidate intents fromintent-bearing utterances within the conversations, while methods likeLatent Semantic Analysis (LSA) may be used to narrow down the salientintents from the utterances. Intent labels, intent alternatives, andintent auxiliaries are then obtained by associating semantically similarsalient intents, which in turn, helps to link utterances to particularintents. As already disclosed, the bot author may use the data mined viageneral intent mining to train NLU models that then power conversationalbots.

It will be appreciated that this general framework of intent miningproceeds from the assumption that the bot author is not aware of theintents that are typically present in the conversation data and/or thatNLU models have not already been developed in relation to the collectionof conversations or similar conversational domains. Thus, the generalintent mining process—for example, a process employing the intent miningengine disclosed above-essentially begins with no prior domain knowledgeand derives or mines intents based solely on the conversational contentof the data.

Many times, however, this assumption does not apply. That is, a botauthor may already have knowledge about the intents in a particulardomain. In such cases, a bot author may understand the intents that aretypically present in certain conversations or expected to be present inspecific conversational domains. This, for example, may be true inbanking or travel domains. Further, in many cases, NLU models may havealready been trained and bots, like travel or banking bots, have alreadybeen published by the bot author. In such scenarios, the existing domainknowledge may be used to guide the intent mining process toward miningspecific intents by using an intent seeding process. As will be seen, aspart of intent mining by seeding, existing domain knowledge is fed intothe mining process in the form of seed intent data. Such seed intentdata may consist of intent labels, which may be referred to as a “seedintent” or “seed intent labels”, and sample utterances associated witheach. The present intent mining process then uses this seed intent datato mine more utterances from the conversation data for each of the seedintents, while also finding utterances for any other salient intentsthat may be found in the conversation data. As stated, this miningprocess is referred to herein as an “intent mining by seeding process”or simply, “intent mining by seeding”.

As will be seen, intent mining by seeding may assist bot authors toquickly identify more utterances belonging to seed intents, which mightbe used to train or improve NLU models. Since such systems can mineother salient intents in addition to the given seed intents, thisprocess may help bot authors identify changing customer intents fordifferent time frames.

As with the general intent mining method discussed above, the process ofintent mining with seeding intents may be initiated via importingconversation data. In general, other steps of the present intent miningby seeding may be the same or similar to those steps disclosed above inrelation to general intent mining. Thus, in the interest of compactexplanation, primary attention will be paid to those areas where intentmining by seeding differs from the general intent mining process thatwas presented above in relation to FIG. 9.

In accordance with the present invention, intent mining by seeding usesseed intent data. Seed intent data, as used herein, includes one or moreseed intents and, for each of the one or more seed intents, a set ofassociated sample utterances. Intent mining by seeding then processesthe seed intent data with conversation data to obtain intentalternatives and/or other utterances for associating with the seedintents. Such intent alternatives are obtained in much the same way asgenerating candidate intents as given in the section above. In thiscase, the seed intent and the associated sample utterances areconsidered as intent-bearing utterances offered by a customer within theconversation data. The normalized action-object pairs obtained from themconstitute the intent alternatives of each seed intent.

Once intent alternatives are obtained for each seed intent, seed intentauxiliaries are identified from the set of candidate intents derivedfrom the conversation data, as provided in the discussion above relatedto FIG. 9. In regard to the step of finding seed intent auxiliaries andassociating utterances and seed intents, the intent mining by seedingprocess may be the same or similar to that described above in thegeneral intent mining process. As in the previous section, the semanticsimilarity technique using embeddings may also be employed here.Similarity may be computed between candidate intents of each of theintent-bearing utterances and the intent alternatives of each seedintent. An intent-bearing utterance is associated with a seed intent if:a) the semantic similarity of any of the intent-bearing utterance'sconstituent candidate intents is the highest with an intent alternativeof that seed intent; and b) the semantic similarity is determined to beabove a minimum threshold (for example, above a score of 0.8). Further,as before, the candidate intent producing the highest similarity scorein relation to one of the seed intents is brought into the seed intentas an “intent auxiliary” or, more specifically, as a “seed intentauxiliary”.

Intent mining by seeding may also include deriving other salient intentsfound within the conversation data that are different from those intentsidentified in the seed intent data. In regard to identifying suchsalient intents, the intent mining by seeding process may be the same orsimilar to that described above in the general intent mining process.That is, candidate intents are identified, and then those within aconceptual group are sorted in relation to weight, with a predeterminednumber of the higher weighted candidate intents from the group beingselected. Of these selected candidate intents, duplicate entries arediscarded, with those entries having the higher weight being the onesthat are kept. In completing this step, the intent mining by seedingprocess may include an additional procedure from that disclosed above inrelation to general intent mining. Specifically, this additionalprocedure includes discarding any of the identified candidate intentsthat are already been identified as seed intent auxiliaries.

The next step is to find out the utterances associated with the minedintents. Like in the previous section, the semantic similarity techniqueusing embeddings is employed here. Similarity is computed betweencandidate intents of an utterance and the intents of all the groups. Anutterance is associated with an intent group if the similarity of any ofits constituent candidate intents is the highest with an intent of thatgroup and is above a minimum threshold (e.g. 0.8). The candidate intentwhich produced the highest similarity is brought into the group and istermed as an “intent auxiliary”. Candidate intents which have alreadybeen identified as seed intent auxiliaries are discarded from thisexercise.

It should be appreciated that, given the functionality discussed abovein regard to intent mining without seeding (i.e., the general intentmining process discussed in relation to FIG. 9) and intent mining withseeding (i.e., the intent mining by seeding process discussed inrelation to FIG. 10), several different use cases or applications arepossible. In a first case, intent mining is performed with no seeding.This can be used to mine salient intents and the associated utterancesthereto from given conversation data. A second case involves a mixedcase in which general intent mining and intent mining by seeding isperformed. As will be appreciated, this case can be used on givenconversation data to mine both salient intents and associated utterancesand more utterances for associating with a given set of seed intents. Ina third case, intent mining with seeding is used to provided focusedmining on a predetermined set of intent seeds. This last case can beused to mine additional utterances for associating with each of the seedintents within the predetermined set.

With specific reference to FIG. 10, a method 600 is provided for intentmining using intent seeds. In exemplary embodiments, the method 600includes an initial step 605 of receiving seed intents. Each of the seedintents includes an intent label and sample intent-bearing utterances.At a step 610, intent-bearing utterances from conversation data areidentified. At a step 615, candidate intents from intent-bearingutterances are selected. At a step 620, seed intent alternatives areidentified from the sample intent-bearing utterances. Then, at a step625, the new utterances are associated with the seed intents. Thesesteps will now be discussed in more detail in the following example.

In accordance with an exemplary embodiment, a computer-implementedmethod for authoring a conversational bot and intent mining using intentseeding is provided. The method may include: receiving conversationdata, the conversation data including text derived from conversations,wherein each of the conversations is between a customer and a customerservice representative; receiving seed intent data that may include seedintents, each of the seed intents including a seed intent label andsample intent-bearing utterances associated with the seed intent; usingan intent mining algorithm to automatically mine the conversation datato determine new utterances to associate with the seed intent;augmenting the seed intent data to include the mined new utterancesassociated with the seed intents; and uploading the augmented seedintent data into the conversation bot and using the conversational botto conduct automated conversations with other customers.

In the case of mining with seed intents, the intent mining algorithm mayinclude analyzing utterances occurring within the conversations of theconversation data to identify intent-bearing utterances. The utteranceseach may include a turn within the conversations whereby the customer,in the form of a customer utterance, or the customer servicerepresentative, in the form of a customer service representativeutterance, is communicating. An intent bearing utterance may be definedas one of the utterances determined to have an increased likelihood ofexpressing an intent. The intent mining algorithm may further includeanalyzing the identified intent-bearing utterances to identify candidateintents. The candidate intents are each identified as being a textphrase occurring within one of the intent-bearing utterances that hastwo parts: an action, which may include a word or phrase describing apurpose or task; and an object, which may include a word or phrasedescribing an object or thing upon which the action operates. The intentmining algorithm may further include, for each of the seed intents,identifying seed intent alternatives from the sample intent-bearingutterances associated with the seed intent. The seed intent alternativesare identified as being a text phrase occurring within one of the sampleintent-bearing utterances that may include two parts: an action, whichmay include a word or phrase describing a purpose or task; and anobject, which may include a word or phrase describing an object or thingupon which the action operates. The intent mining algorithm may furtherinclude associating the intent-bearing utterances from the conversationdata with the seed intents via determining a degree of semanticsimilarity between the candidate intents present in the intent-bearingutterances and the seed intent alternatives belonging to each of theseed intent labels.

In accordance with exemplary embodiments, step of identifying theintent-bearing utterances may include selecting a first portion of thecustomer utterances as the intent-bearing utterances and discarding asecond portion of the customer utterances within the conversation data.The first portion of customer utterances may be defined as apredetermined number of consecutive customer utterances occurring at abeginning of each of the conversations, and the second portion may bedefined as the remainder of each of the conversations. The step ofidentifying the intent-bearing utterances further may include discardingthe customer utterances in the first portion of customer utterances thatfail to satisfy word-count constraints. The word-count constraints mayinclude: a minimum word count constraint in which the customerutterances in the first portion of customer utterances having less wordsthan the minimum word count constraint are discarded; and/or a maximumword count constraint in which the customer utterances in the firstportion of customer utterances having more words than the maximum wordcount constraint are discarded.

In accordance with exemplary embodiments, step of identifying candidateintents may include: using a syntactic dependency parser to analyze agrammatical structure of the intent-bearing utterance to identifyhead-token pairs, each head-token pair including a head word modified bya token word; and using parts-of-speech (hereinafter “POS”) tagging totag parts of speech of the intent-bearing utterances and identifying asthe candidate intents the head-token pairs in which the POS tag of thehead word may include a noun tag and the POS tag of the token word mayinclude a verb tag.

In accordance with exemplary embodiments, step of identifying seedintent alternatives may include using a syntactic dependency parser toanalyze a grammatical structure of the sample intent-bearing utterancesto identify head-token pairs, each head-token pair including a head wordmodified by a token word; and using parts-of-speech (hereafter “POS”)tagging to tag parts of speech of the sample intent-bearing utterancesand identifying as the candidate intents the head-token pairs in whichthe POS tag of the head word may include a noun tag and the POS tag ofthe token word may include a verb tag.

In accordance with exemplary embodiments, step of associating theintent-bearing utterances from the conversation data with the seedintents may include performing a first process repetitively to covereach of the intent-bearing utterances in relation to each of the seedintents, wherein, if described in relation to an exemplary first caseinvolving first and second seed intents and a first intent-bearingutterances that contains first and second candidate intents. The firstprocess may include: computing a degree of semantic similarity betweeneach of the first and second candidate intents and each of the intentalternatives in the first seed intent; computing a degree of semanticsimilarity between each of the first and second candidate intents andeach of the intent alternatives in the second seed intent; determiningwhich of the intent alternatives produced the highest computed degree ofsemantic similarity; and associating the first intent-bearing utterancewith whichever of the first and second seed intents contains the intentalternative that was determined to produce the highest computed degreeof semantic similarity.

In an alternative use case, the method of the present invention includesusing the intent mining algorithm to automatically mine new intentsalong with mining new utterances for associating with a given set ofseed intents. In such cases, the method may include augmenting the seedintent data to include the mined new intents. In this case, the intentmining algorithm may further include: in accordance with one or morecriteria, selecting salient intents from the candidate intents presentin the intent-bearing utterances that are not already associated withone of the seed intents (hereinafter “unassociated intent bearingutterances”); grouping the selected salient intents into salient intentgroups in accordance with a degree of semantic similarity between thesalient intents; for each of the salient intent groups, selecting one ofthe salient intents as the intent label and designating the othersalient intents as intent alternatives; and associating the unassociatedintent-bearing utterances from the conversation data with the salientintent groups via determining a degree of semantic similarity betweenthe candidate intents present in the unassociated intent-bearingutterances and the intent alternatives within each of the salient intentgroups. The new mined intents each may include a given one of thesalient intent groups, each of which being defined by: the one of thesalient intents that is selected as the intent label and the other ofthe salient intents that are designated as the alternative intents; andthe unassociated intent-bearing utterances that become associated withthe given one of the salient intent groups.

In accordance with exemplary embodiments, step of identifying candidateintents may include: using a syntactic dependency parser to analyze agrammatical structure of the intent-bearing utterance to identifyhead-token pairs, each head-token pair including a head word modified bya token word; and using parts-of-speech (hereinafter “POS”) tagging totag parts of speech of the intent-bearing utterances and identifying asthe candidate intents the head-token pairs in which the POS tag of thehead word may include a noun tag and the POS tag of the token word mayinclude a verb tag.

In accordance with exemplary embodiments, one or more criteria by whichthe salient intents are selected from the candidate intents may includea criterion based on Latent Semantic Analysis (LSA). The step ofselecting the salient intents from the candidate intents may include:generating a set of documents having documents corresponding torespective ones of the candidate intents, wherein each of the documentscovers an action-object pair defined by the corresponding one of thecandidate intents; generating conceptual groups based on terms appearingin the action-object pairs contained in the set of documents;calculating a weight value for each of the candidate intents for each ofthe conceptual groups, the weight value measuring a degree ofrelatedness between the candidate intent of a given one of the documentsand a given one of the conceptual groups; and selecting as the salientintents a predetermined number of the candidate intents in each of theconceptual groups based on which produce weight values indicating ahigher degree of relatedness.

In accordance with exemplary embodiments, step of grouping of thesalient intents in accordance with the degree of semantic similarity mayinclude: calculating an embedding for each of the salient intents,wherein an embedding may include an encoded representation of text inwhich texts that are semantically similar have similar encodedrepresentations; comparing the calculated embeddings to determining thedegree of semantic similarity between pairs of the salient intents; andgrouping the salient intents having a degree of semantic similarityabove a predetermined threshold. The embedding is calculated as anInverse Document Frequency (IDF) average of Global Vectors embeddings ofthe constituent head-token pairs of the salient intent. The comparingthe calculated embeddings may include cosine similarity.

In accordance with exemplary embodiments, step of associating theunassociated intent-bearing utterances from the conversation data withthe salient intent groups may include performing a first processrepetitively to cover each of the unassociated intent-bearing utterancesin relation to each of the salient intent groups. If described inrelation to an exemplary first case involving first and second salientintent groups and a first unassociated intent-bearing utterances thatcontains first and second candidate intents, the first process mayinclude: computing a degree of semantic similarity between each of thefirst and second candidate intents and each of the intent alternativesin the first salient intent group; computing a degree of semanticsimilarity between each of the first and second candidate intents andeach of the intent alternatives in the second salient intent group;determining which of the intent alternatives produced the highestcomputed degree of semantic similarity; and associating the firstunassociated intent-bearing utterance with whichever of the first andsecond salient intent groups contains the intent alternative that wasdetermined to produce the highest computed degree of semanticsimilarity.

As one of skill in the art will appreciate, the many varying featuresand configurations described above in relation to the several exemplaryembodiments may be further selectively applied to form the otherpossible embodiments of the present invention. For the sake of brevityand taking into account the abilities of one of ordinary skill in theart, each of the possible iterations is not provided or discussed indetail, though all combinations and possible embodiments embraced by theseveral claims below or otherwise are intended to be part of the instantapplication. In addition, from the above description of severalexemplary embodiments of the invention, those skilled in the art willperceive improvements, changes and modifications. Such improvements,changes and modifications within the skill of the art are also intendedto be covered by the appended claims. Further, it should be apparentthat the foregoing relates only to the described embodiments of thepresent application and that numerous changes and modifications may bemade herein without departing from the spirit and scope of the presentapplication as defined by the following claims and the equivalentsthereof

That which is claimed:
 1. A computer-implemented method for authoring aconversational bot comprising: receiving conversation data, theconversation data comprising text derived from conversations, whereineach of the conversations is between a customer and a customer servicerepresentative; receiving seed intent data that comprises seed intents,each of the seed intents comprising a seed intent label and sampleintent-bearing utterances associated with the seed intent; using anintent mining algorithm to automatically mine the conversation data todetermine new utterances to associate with the seed intent; augmentingthe seed intent data to include the mined new utterances associated withthe seed intents; and uploading the augmented seed intent data into theconversation bot and using the conversational bot to conduct automatedconversations with other customers; wherein the intent mining algorithmcomprises: analyzing utterances occurring within the conversations ofthe conversation data to identify intent-bearing utterances, wherein:the utterances each comprise a turn within the conversations whereby thecustomer, in the form of a customer utterance, or the customer servicerepresentative, in the form of a customer service representativeutterance, is communicating; and an intent bearing utterance is definedas one of the utterances determined to have an increased likelihood ofexpressing an intent; analyzing the identified intent-bearing utterancesto identify candidate intents, wherein the candidate intents are eachidentified as being a text phrase occurring within one of theintent-bearing utterances that has two parts: an action, which comprisesa word or phrase describing a purpose or task, and an object, whichcomprises a word or phrase describing an object or thing upon which theaction operates; for each of the seed intents, identifying seed intentalternatives from the sample intent-bearing utterances associated withthe seed intent, wherein the seed intent alternatives are identified asbeing a text phrase occurring within one of the sample intent-bearingutterances that includes two parts: an action, which comprises a word orphrase describing a purpose or task, and an object, which comprises aword or phrase describing an object or thing upon which the actionoperates; associating the intent-bearing utterances from theconversation data with the seed intents via determining a degree ofsemantic similarity between: the candidate intents present in theintent-bearing utterances; and the seed intent alternatives belonging toeach of the seed intent labels.
 2. The method of claim 1, wherein theidentifying the intent-bearing utterances comprises selecting a firstportion of the customer utterances as the intent-bearing utterances anddiscarding a second portion of the customer utterances within theconversation data; and wherein the first portion of customer utterancesis defined as a predetermined number of consecutive customer utterancesoccurring at a beginning of each of the conversations, and the secondportion is defined as the remainder of each of the conversations.
 3. Themethod of claim 2, wherein the identifying the intent-bearing utterancesfurther comprises discarding the customer utterances in the firstportion of customer utterances that fail to satisfy word-countconstraints; wherein the word-count constraints comprise: a minimum wordcount constraint in which the customer utterances in the first portionof customer utterances having less words than the minimum word countconstraint are discarded; and a maximum word count constraint in whichthe customer utterances in the first portion of customer utteranceshaving more words than the maximum word count constraint are discarded.4. The method of claim 1, wherein the identifying candidate intentscomprises: using a syntactic dependency parser to analyze a grammaticalstructure of the intent-bearing utterance to identify head-token pairs,each head-token pair comprising a head word modified by a token word;using parts-of-speech (hereinafter “POS”) tagging to tag parts of speechof the intent-bearing utterances and identifying as the candidateintents the head-token pairs in which the POS tag of the head wordcomprises a noun tag and the POS tag of the token word comprise a verbtag.
 5. The method of claim 1, wherein the identifying seed intentalternatives comprises: using a syntactic dependency parser to analyze agrammatical structure of the sample intent-bearing utterances toidentify head-token pairs, each head-token pair comprising a head wordmodified by a token word; using parts-of-speech (hereafter “POS”)tagging to tag parts of speech of the sample intent-bearing utterancesand identifying as the candidate intents the head-token pairs in whichthe POS tag of the head word comprises a noun tag and the POS tag of thetoken word comprise a verb tag.
 6. The method of claim 5, wherein theassociating the intent-bearing utterances from the conversation datawith the seed intents comprises performing a first process repetitivelyto cover each of the intent-bearing utterances in relation to each ofthe seed intents, wherein, if described in relation to an exemplaryfirst case involving first and second seed intents and a firstintent-bearing utterances that contains first and second candidateintents, the first process includes: computing a degree of semanticsimilarity between each of the first and second candidate intents andeach of the intent alternatives in the first seed intent; computing adegree of semantic similarity between each of the first and secondcandidate intents and each of the intent alternatives in the second seedintent; determining which of the intent alternatives produced thehighest computed degree of semantic similarity; and associating thefirst intent-bearing utterance with whichever of the first and secondseed intents contains the intent alternative that was determined toproduce the highest computed degree of semantic similarity.
 7. Themethod of to claim 1, further comprising: using the intent miningalgorithm to automatically mine new intents, each of the mined newintents comprising an intent label, intent alternatives, and associatedutterances; and augmenting the seed intent data to include the mined newintents; wherein the intent mining algorithm further comprises: inaccordance with one or more criteria, selecting salient intents from thecandidate intents present in the intent-bearing utterances that are notalready associated with one of the seed intents (hereinafter“unassociated intent bearing utterances”); grouping the selected salientintents into salient intent groups in accordance with a degree ofsemantic similarity between the salient intents; for each of the salientintent groups, selecting one of the salient intents as the intent labeland designating the other salient intents as intent alternatives; andassociating the unassociated intent-bearing utterances from theconversation data with the salient intent groups via determining adegree of semantic similarity between: the candidate intents present inthe unassociated intent-bearing utterances; and the intent alternativeswithin each of the salient intent groups.
 8. The method of claim 7,wherein the new mined intents each comprises: a given one of the salientintent groups, each of which being defined by: the one of the salientintents that is selected as the intent label; and the other of thesalient intents that are designated as the alternative intents; and theunassociated intent-bearing utterances that become associated with thegiven one of the salient intent groups.
 9. The method of claim 8,wherein the identifying candidate intents comprises: using a syntacticdependency parser to analyze a grammatical structure of theintent-bearing utterance to identify head-token pairs, each head-tokenpair comprising a head word modified by a token word; and usingparts-of-speech (hereinafter “POS”) tagging to tag parts of speech ofthe intent-bearing utterances and identifying as the candidate intentsthe head-token pairs in which the POS tag of the head word comprises anoun tag and the POS tag of the token word comprise a verb tag.
 10. Themethod of claim 9, wherein the selecting the salient intents from thecandidate intents comprises: generating a set of documents havingdocuments corresponding to respective ones of the candidate intents,wherein each of the documents covers an action-object pair defined bythe corresponding one of the candidate intents; generating conceptualgroups based on terms appearing in the action-object pairs contained inthe set of documents; calculating a weight value for each of thecandidate intents for each of the conceptual groups, the weight valuemeasuring a degree of relatedness between the candidate intent of agiven one of the documents and a given one of the conceptual groups; andselecting as the salient intents a predetermined number of the candidateintents in each of the conceptual groups based on which produce weightvalues indicating a higher degree of relatedness.
 11. The method ofclaim 10, wherein the grouping of the salient intents in accordance withthe degree of semantic similarity comprises: calculating an embeddingfor each of the salient intents, wherein an embedding comprises anencoded representation of text in which texts that are semanticallysimilar have similar encoded representations; comparing the calculatedembeddings to determining the degree of semantic similarity betweenpairs of the salient intents; and grouping the salient intents having adegree of semantic similarity above a predetermined threshold.
 12. Themethod of claim 11, wherein the embedding is calculated as an InverseDocument Frequency average of Global Vectors embeddings of theconstituent head-token pairs of the salient intent; and wherein thecomparing the calculated embeddings comprises cosine similarity.
 13. Themethod of claim 8, wherein the associating the unassociatedintent-bearing utterances from the conversation data with the salientintent groups comprises performing a first process repetitively to covereach of the unassociated intent-bearing utterances in relation to eachof the salient intent groups, wherein, if described in relation to anexemplary first case involving first and second salient intent groupsand a first unassociated intent-bearing utterances that contains firstand second candidate intents, the first process includes: computing adegree of semantic similarity between each of the first and secondcandidate intents and each of the intent alternatives in the firstsalient intent group; computing a degree of semantic similarity betweeneach of the first and second candidate intents and each of the intentalternatives in the second salient intent group; determining which ofthe intent alternatives produced the highest computed degree of semanticsimilarity; and associating the first unassociated intent-bearingutterance with whichever of the first and second salient intent groupscontains the intent alternative that was determined to produce thehighest computed degree of semantic similarity.
 14. A system forautomating aspects of authoring a conversational bot, the systemcomprising: a processor; and a memory, wherein the memory storesinstructions that, when executed by the processor, cause the processorto perform: receiving conversation data, the conversation datacomprising text derived from conversations, wherein each of theconversations is between a customer and a customer servicerepresentative; receiving seed intent data that comprises seed intents,each of the seed intents comprising a seed intent label and sampleintent-bearing utterances associated with the seed intent; using anintent mining algorithm to automatically mine the conversation data todetermine new utterances to associate with the seed intent; augmentingthe seed intent data to include the mined new utterances associated withthe seed intents; and uploading the augmented seed intent data into theconversation bot and using the conversational bot to conduct automatedconversations with other customers; wherein the intent mining algorithmcomprises: analyzing utterances occurring within the conversations ofthe conversation data to identify intent-bearing utterances, wherein:the utterances each comprise a turn within the conversations whereby thecustomer, in the form of a customer utterance, or the customer servicerepresentative, in the form of a customer service representativeutterance, is communicating; and an intent bearing utterance is definedas one of the utterances determined to have an increased likelihood ofexpressing an intent; analyzing the identified intent-bearing utterancesto identify candidate intents, wherein the candidate intents are eachidentified as being a text phrase occurring within one of theintent-bearing utterances that has two parts: an action, which comprisesa word or phrase describing a purpose or task, and an object, whichcomprises a word or phrase describing an object or thing upon which theaction operates; for each of the seed intents, identifying seed intentalternatives from the sample intent-bearing utterances associated withthe seed intent, wherein the seed intent alternatives are identified asbeing a text phrase occurring within one of the sample intent-bearingutterances that includes two parts: an action, which comprises a word orphrase describing a purpose or task, and an object, which comprises aword or phrase describing an object or thing upon which the actionoperates; and associating the intent-bearing utterances from theconversation data with the seed intents via determining a degree ofsemantic similarity between: the candidate intents present in theintent-bearing utterances; and the seed intent alternatives belonging toeach of the seed intent labels.
 15. The system of claim 14, wherein theidentifying the intent-bearing utterances comprises selecting a firstportion of the customer utterances as the intent-bearing utterances anddiscarding a second portion of the customer utterances within theconversation data; and wherein the first portion of customer utterancesis defined as a predetermined number of consecutive customer utterancesoccurring at a beginning of each of the conversations, and the secondportion is defined as the remainder of each of the conversations. 16.The system of claim 15, wherein the identifying the intent-bearingutterances further comprises discarding the customer utterances in thefirst portion of customer utterances that fail to satisfy word-countconstraints; wherein the word-count constraints comprise: a minimum wordcount constraint in which the customer utterances in the first portionof customer utterances having less words than the minimum word countconstraint are discarded; and a maximum word count constraint in whichthe customer utterances in the first portion of customer utteranceshaving more words than the maximum word count constraint are discarded.17. The system of claim 14, wherein the identifying candidate intentscomprises: using a syntactic dependency parser to analyze a grammaticalstructure of the intent-bearing utterance to identify head-token pairs,each head-token pair comprising a head word modified by a token word;using parts-of-speech (hereinafter “POS”) tagging to tag parts of speechof the intent-bearing utterances and identifying as the candidateintents the head-token pairs in which the POS tag of the head wordcomprises a noun tag and the POS tag of the token word comprise a verbtag.
 18. The system of claim 14, wherein the identifying seed intentalternatives comprises: using a syntactic dependency parser to analyze agrammatical structure of the sample intent-bearing utterances toidentify head-token pairs, each head-token pair comprising a head wordmodified by a token word; using parts-of-speech (hereafter “POS”)tagging to tag parts of speech of the sample intent-bearing utterancesand identifying as the candidate intents the head-token pairs in whichthe POS tag of the head word comprises a noun tag and the POS tag of thetoken word comprise a verb tag.
 19. The system of claim 18, wherein theassociating the intent-bearing utterances from the conversation datawith the seed intents comprises performing a first process repetitivelyto cover each of the intent-bearing utterances in relation to each ofthe seed intents, wherein, if described in relation to an exemplaryfirst case involving first and second seed intents and a firstintent-bearing utterances that contains first and second candidateintents, the first process includes: computing a degree of semanticsimilarity between each of the first and second candidate intents andeach of the intent alternatives in the first seed intent; computing adegree of semantic similarity between each of the first and secondcandidate intents and each of the intent alternatives in the second seedintent; determining which of the intent alternatives produced thehighest computed degree of semantic similarity; and associating thefirst intent-bearing utterance with whichever of the first and secondseed intents contains the intent alternative that was determined toproduce the highest computed degree of semantic similarity.
 20. Thesystem of to claim 14, further comprising: using the intent miningalgorithm to automatically mine new intents, each of the mined newintents comprising an intent label, intent alternatives, and associatedutterances; and augmenting the seed intent data to include the mined newintents; wherein the intent mining algorithm further comprises: inaccordance with one or more criteria, selecting salient intents from thecandidate intents present in the intent-bearing utterances that are notalready associated with one of the seed intents (hereinafter“unassociated intent bearing utterances”); grouping the selected salientintents into salient intent groups in accordance with a degree ofsemantic similarity between the salient intents; for each of the salientintent groups, selecting one of the salient intents as the intent labeland designating the other salient intents as intent alternatives; andassociating the unassociated intent-bearing utterances from theconversation data with the salient intent groups via determining adegree of semantic similarity between: the candidate intents present inthe unassociated intent-bearing utterances; and the intent alternativeswithin each of the salient intent groups.
 21. The system of claim 20,wherein the new mined intents each comprises: a given one of the salientintent groups, each of which being defined by: the one of the salientintents that is selected as the intent label; and the other of thesalient intents that are designated as the alternative intents; and theunassociated intent-bearing utterances that become associated with thegiven one of the salient intent groups.
 22. The system of claim 21,wherein the identifying candidate intents comprises: using a syntacticdependency parser to analyze a grammatical structure of theintent-bearing utterance to identify head-token pairs, each head-tokenpair comprising a head word modified by a token word; usingparts-of-speech (hereinafter “POS”) tagging to tag parts of speech ofthe intent-bearing utterances and identifying as the candidate intentsthe head-token pairs in which the POS tag of the head word comprises anoun tag and the POS tag of the token word comprise a verb tag.
 23. Thesystem of claim 22, wherein the selecting the salient intents from thecandidate intents comprises: generating a set of documents havingdocuments corresponding to respective ones of the candidate intents,wherein each of the documents covers an action-object pair defined bythe corresponding one of the candidate intents; generating conceptualgroups based on terms appearing in the action-object pairs contained inthe set of documents; calculating a weight value for each of thecandidate intents for each of the conceptual groups, the weight valuemeasuring a degree of relatedness between the candidate intent of agiven one of the documents and a given one of the conceptual groups; andselecting as the salient intents a predetermined number of the candidateintents in each of the conceptual groups based on which produce weightvalues indicating a higher degree of relatedness.
 24. The system ofclaim 23, wherein the grouping of the salient intents in accordance withthe degree of semantic similarity comprises: calculating an embeddingfor each of the salient intents, wherein an embedding comprises anencoded representation of text in which texts that are semanticallysimilar have similar encoded representations; comparing the calculatedembeddings to determining the degree of semantic similarity betweenpairs of the salient intents; and grouping the salient intents having adegree of semantic similarity above a predetermined threshold.
 25. Thesystem of claim 24, wherein the embedding is calculated as an InverseDocument Frequency average of Global Vectors embeddings of theconstituent head-token pairs of the salient intent; and wherein thecomparing the calculated embeddings comprises cosine similarity.
 26. Thesystem of claim 21, wherein the associating the unassociatedintent-bearing utterances from the conversation data with the salientintent groups comprises performing a first process repetitively to covereach of the unassociated intent-bearing utterances in relation to eachof the salient intent groups, wherein, if described in relation to anexemplary first case involving first and second salient intent groupsand a first unassociated intent-bearing utterances that contains firstand second candidate intents, the first process includes: computing adegree of semantic similarity between each of the first and secondcandidate intents and each of the intent alternatives in the firstsalient intent group; computing a degree of semantic similarity betweeneach of the first and second candidate intents and each of the intentalternatives in the second salient intent group; determining which ofthe intent alternatives produced the highest computed degree of semanticsimilarity; and associating the first unassociated intent-bearingutterance with whichever of the first and second salient intent groupscontains the intent alternative that was determined to produce thehighest computed degree of semantic similarity.